NP URI: www.nobleprog.com.ec/en/cc/mlbigdata Undefined array key "mysql_identifier_quote_character" /apps/nobleprog-website/includes/functions/new-modules-general-functions.php:82 Array ( [0] => Array ( [file] => /apps/nobleprog-website/includes/functions/new-modules-general-functions.php [line] => 82 [function] => myErrorHandler [args] => Array ( [0] => 2 [1] => Undefined array key "mysql_identifier_quote_character" [2] => /apps/nobleprog-website/includes/functions/new-modules-general-functions.php [3] => 82 ) ) [1] => Array ( [file] => /apps/hitra7/drupal7/includes/database/mysql/database.inc [line] => 397 [function] => variable_get [args] => Array ( [0] => mysql_identifier_quote_character [1] => ` ) ) [2] => Array ( [file] => /apps/hitra7/drupal7/includes/database/database.inc [line] => 329 [function] => setPrefix [class] => DatabaseConnection_mysql [object] => DatabaseConnection_mysql Object ( [target:protected] => [key:protected] => [logger:protected] => [transactionLayers:protected] => Array ( ) [driverClasses:protected] => Array ( ) [statementClass:protected] => DatabaseStatementBase [transactionSupport:protected] => 1 [transactionalDDLSupport:protected] => [temporaryNameIndex:protected] => 0 [connection:protected] => [connectionOptions:protected] => Array ( [driver] => mysql [database] => hitrahr [username] => root [password] => asdf [host] => localhost [prefix] => Array ( [default] => ) ) [schema:protected] => [prefixes:protected] => Array ( [default] => ) [prefixSearch:protected] => Array ( [0] => { [1] => } ) [prefixReplace:protected] => Array ( [0] => [1] => ) [escapedNames:protected] => Array ( ) [escapedAliases:protected] => Array ( ) [unprefixedTablesMap:protected] => Array ( ) [needsCleanup:protected] => [reservedKeyWords:DatabaseConnection_mysql:private] => Array ( [0] => accessible [1] => add [2] => admin [3] => all [4] => alter [5] => analyze [6] => and [7] => as [8] => asc [9] => asensitive [10] => before [11] => between [12] => bigint [13] => binary [14] => blob [15] => both [16] => by [17] => call [18] => cascade [19] => case [20] => change [21] => char [22] => character [23] => check [24] => collate [25] => column [26] => condition [27] => constraint [28] => continue [29] => convert [30] => create [31] => cross [32] => cube [33] => cume_dist [34] => current_date [35] => current_time [36] => current_timestamp [37] => current_user [38] => cursor [39] => database [40] => databases [41] => day_hour [42] => day_microsecond [43] => day_minute [44] => day_second [45] => dec [46] => decimal [47] => declare [48] => default [49] => delayed [50] => delete [51] => dense_rank [52] => desc [53] => describe [54] => deterministic [55] => distinct [56] => distinctrow [57] => div [58] => double [59] => drop [60] => dual [61] => each [62] => else [63] => elseif [64] => empty [65] => enclosed [66] => escaped [67] => except [68] => exists [69] => exit [70] => explain [71] => false [72] => fetch [73] => first_value [74] => float [75] => float4 [76] => float8 [77] => for [78] => force [79] => foreign [80] => from [81] => fulltext [82] => function [83] => generated [84] => get [85] => grant [86] => group [87] => grouping [88] => groups [89] => having [90] => high_priority [91] => hour_microsecond [92] => hour_minute [93] => hour_second [94] => if [95] => ignore [96] => in [97] => index [98] => infile [99] => inner [100] => inout [101] => insensitive [102] => insert [103] => int [104] => int1 [105] => int2 [106] => int3 [107] => int4 [108] => int8 [109] => integer [110] => intersect [111] => interval [112] => into [113] => io_after_gtids [114] => io_before_gtids [115] => is [116] => iterate [117] => join [118] => json_table [119] => key [120] => keys [121] => kill [122] => lag [123] => last_value [124] => lateral [125] => lead [126] => leading [127] => leave [128] => left [129] => like [130] => limit [131] => linear [132] => lines [133] => load [134] => localtime [135] => localtimestamp [136] => lock [137] => long [138] => longblob [139] => longtext [140] => loop [141] => low_priority [142] => master_bind [143] => master_ssl_verify_server_cert [144] => match [145] => maxvalue [146] => mediumblob [147] => mediumint [148] => mediumtext [149] => middleint [150] => minute_microsecond [151] => minute_second [152] => mod [153] => modifies [154] => natural [155] => not [156] => no_write_to_binlog [157] => nth_value [158] => ntile [159] => null [160] => numeric [161] => of [162] => on [163] => optimize [164] => optimizer_costs [165] => option [166] => optionally [167] => or [168] => order [169] => out [170] => outer [171] => outfile [172] => over [173] => partition [174] => percent_rank [175] => persist [176] => persist_only [177] => precision [178] => primary [179] => procedure [180] => purge [181] => range [182] => rank [183] => read [184] => reads [185] => read_write [186] => real [187] => recursive [188] => references [189] => regexp [190] => release [191] => rename [192] => repeat [193] => replace [194] => require [195] => resignal [196] => restrict [197] => return [198] => revoke [199] => right [200] => rlike [201] => row [202] => rows [203] => row_number [204] => schema [205] => schemas [206] => second_microsecond [207] => select [208] => sensitive [209] => separator [210] => set [211] => show [212] => signal [213] => smallint [214] => spatial [215] => specific [216] => sql [217] => sqlexception [218] => sqlstate [219] => sqlwarning [220] => sql_big_result [221] => sql_calc_found_rows [222] => sql_small_result [223] => ssl [224] => starting [225] => stored [226] => straight_join [227] => system [228] => table [229] => terminated [230] => then [231] => tinyblob [232] => tinyint [233] => tinytext [234] => to [235] => trailing [236] => trigger [237] => true [238] => undo [239] => union [240] => unique [241] => unlock [242] => unsigned [243] => update [244] => usage [245] => use [246] => using [247] => utc_date [248] => utc_time [249] => utc_timestamp [250] => values [251] => varbinary [252] => varchar [253] => varcharacter [254] => varying [255] => virtual [256] => when [257] => where [258] => while [259] => window [260] => with [261] => write [262] => xor [263] => year_month [264] => zerofill ) ) [type] => -> [args] => Array ( [0] => Array ( [default] => ) ) ) [3] => Array ( [file] => /apps/hitra7/drupal7/includes/database/mysql/database.inc [line] => 349 [function] => __construct [class] => DatabaseConnection [object] => DatabaseConnection_mysql Object ( [target:protected] => [key:protected] => [logger:protected] => [transactionLayers:protected] => Array ( ) [driverClasses:protected] => Array ( ) [statementClass:protected] => DatabaseStatementBase [transactionSupport:protected] => 1 [transactionalDDLSupport:protected] => [temporaryNameIndex:protected] => 0 [connection:protected] => [connectionOptions:protected] => Array ( [driver] => mysql [database] => hitrahr [username] => root [password] => asdf [host] => localhost [prefix] => Array ( [default] => ) ) [schema:protected] => [prefixes:protected] => Array ( [default] => ) [prefixSearch:protected] => Array ( [0] => { [1] => } ) [prefixReplace:protected] => Array ( [0] => [1] => ) [escapedNames:protected] => Array ( ) [escapedAliases:protected] => Array ( ) [unprefixedTablesMap:protected] => Array ( ) [needsCleanup:protected] => [reservedKeyWords:DatabaseConnection_mysql:private] => Array ( [0] => accessible [1] => add [2] => admin [3] => all [4] => alter [5] => analyze [6] => and [7] => as [8] => asc [9] => asensitive [10] => before [11] => between [12] => bigint [13] => binary [14] => blob [15] => both [16] => by [17] => call [18] => cascade [19] => case [20] => change [21] => char [22] => character [23] => check [24] => collate [25] => column [26] => condition [27] => constraint [28] => continue [29] => convert [30] => create [31] => cross [32] => cube [33] => cume_dist [34] => current_date [35] => current_time [36] => current_timestamp [37] => current_user [38] => cursor [39] => database [40] => databases [41] => day_hour [42] => day_microsecond [43] => day_minute [44] => day_second [45] => dec [46] => decimal [47] => declare [48] => default [49] => delayed [50] => delete [51] => dense_rank [52] => desc [53] => describe [54] => deterministic [55] => distinct [56] => distinctrow [57] => div [58] => double [59] => drop [60] => dual [61] => each [62] => else [63] => elseif [64] => empty [65] => enclosed [66] => escaped [67] => except [68] => exists [69] => exit [70] => explain [71] => false [72] => fetch [73] => first_value [74] => float [75] => float4 [76] => float8 [77] => for [78] => force [79] => foreign [80] => from [81] => fulltext [82] => function [83] => generated [84] => get [85] => grant [86] => group [87] => grouping [88] => groups [89] => having [90] => high_priority [91] => hour_microsecond [92] => hour_minute [93] => hour_second [94] => if [95] => ignore [96] => in [97] => index [98] => infile [99] => inner [100] => inout [101] => insensitive [102] => insert [103] => int [104] => int1 [105] => int2 [106] => int3 [107] => int4 [108] => int8 [109] => integer [110] => intersect [111] => interval [112] => into [113] => io_after_gtids [114] => io_before_gtids [115] => is [116] => iterate [117] => join [118] => json_table [119] => key [120] => keys [121] => kill [122] => lag [123] => last_value [124] => lateral [125] => lead [126] => leading [127] => leave [128] => left [129] => like [130] => limit [131] => linear [132] => lines [133] => load [134] => localtime [135] => localtimestamp [136] => lock [137] => long [138] => longblob [139] => longtext [140] => loop [141] => low_priority [142] => master_bind [143] => master_ssl_verify_server_cert [144] => match [145] => maxvalue [146] => mediumblob [147] => mediumint [148] => mediumtext [149] => middleint [150] => minute_microsecond [151] => minute_second [152] => mod [153] => modifies [154] => natural [155] => not [156] => no_write_to_binlog [157] => nth_value [158] => ntile [159] => null [160] => numeric [161] => of [162] => on [163] => optimize [164] => optimizer_costs [165] => option [166] => optionally [167] => or [168] => order [169] => out [170] => outer [171] => outfile [172] => over [173] => partition [174] => percent_rank [175] => persist [176] => persist_only [177] => precision [178] => primary [179] => procedure [180] => purge [181] => range [182] => rank [183] => read [184] => reads [185] => read_write [186] => real [187] => recursive [188] => references [189] => regexp [190] => release [191] => rename [192] => repeat [193] => replace [194] => require [195] => resignal [196] => restrict [197] => return [198] => revoke [199] => right [200] => rlike [201] => row [202] => rows [203] => row_number [204] => schema [205] => schemas [206] => second_microsecond [207] => select [208] => sensitive [209] => separator [210] => set [211] => show [212] => signal [213] => smallint [214] => spatial [215] => specific [216] => sql [217] => sqlexception [218] => sqlstate [219] => sqlwarning [220] => sql_big_result [221] => sql_calc_found_rows [222] => sql_small_result [223] => ssl [224] => starting [225] => stored [226] => straight_join [227] => system [228] => table [229] => terminated [230] => then [231] => tinyblob [232] => tinyint [233] => tinytext [234] => to [235] => trailing [236] => trigger [237] => true [238] => undo [239] => union [240] => unique [241] => unlock [242] => unsigned [243] => update [244] => usage [245] => use [246] => using [247] => utc_date [248] => utc_time [249] => utc_timestamp [250] => values [251] => varbinary [252] => varchar [253] => varcharacter [254] => varying [255] => virtual [256] => when [257] => where [258] => while [259] => window [260] => with [261] => write [262] => xor [263] => year_month [264] => zerofill ) ) [type] => -> [args] => Array ( [0] => mysql:host=localhost;port=3306;charset=utf8;dbname=hitrahr [1] => root [2] => asdf [3] => Array ( [1000] => 1 [20] => 1 [17] => 1 [1013] => ) ) ) [4] => Array ( [file] => /apps/hitra7/drupal7/includes/database/database.inc [line] => 1796 [function] => __construct [class] => DatabaseConnection_mysql [object] => DatabaseConnection_mysql Object ( [target:protected] => [key:protected] => [logger:protected] => [transactionLayers:protected] => Array ( ) [driverClasses:protected] => Array ( ) [statementClass:protected] => DatabaseStatementBase [transactionSupport:protected] => 1 [transactionalDDLSupport:protected] => [temporaryNameIndex:protected] => 0 [connection:protected] => [connectionOptions:protected] => Array ( [driver] => mysql [database] => hitrahr [username] => root [password] => asdf [host] => localhost [prefix] => Array ( [default] => ) ) [schema:protected] => [prefixes:protected] => Array ( [default] => ) [prefixSearch:protected] => Array ( [0] => { [1] => } ) [prefixReplace:protected] => Array ( [0] => [1] => ) [escapedNames:protected] => Array ( ) [escapedAliases:protected] => Array ( ) [unprefixedTablesMap:protected] => Array ( ) [needsCleanup:protected] => [reservedKeyWords:DatabaseConnection_mysql:private] => Array ( [0] => accessible [1] => add [2] => admin [3] => all [4] => alter [5] => analyze [6] => and [7] => as [8] => asc [9] => asensitive [10] => before [11] => between [12] => bigint [13] => binary [14] => blob [15] => both [16] => by [17] => call [18] => cascade [19] => case [20] => change [21] => char [22] => character [23] => check [24] => collate [25] => column [26] => condition [27] => constraint [28] => continue [29] => convert [30] => create [31] => cross [32] => cube [33] => cume_dist [34] => current_date [35] => current_time [36] => current_timestamp [37] => current_user [38] => cursor [39] => database [40] => databases [41] => day_hour [42] => day_microsecond [43] => day_minute [44] => day_second [45] => dec [46] => decimal [47] => declare [48] => default [49] => delayed [50] => delete [51] => dense_rank [52] => desc [53] => describe [54] => deterministic [55] => distinct [56] => distinctrow [57] => div [58] => double [59] => drop [60] => dual [61] => each [62] => else [63] => elseif [64] => empty [65] => enclosed [66] => escaped [67] => except [68] => exists [69] => exit [70] => explain [71] => false [72] => fetch [73] => first_value [74] => float [75] => float4 [76] => float8 [77] => for [78] => force [79] => foreign [80] => from [81] => fulltext [82] => function [83] => generated [84] => get [85] => grant [86] => group [87] => grouping [88] => groups [89] => having [90] => high_priority [91] => hour_microsecond [92] => hour_minute [93] => hour_second [94] => if [95] => ignore [96] => in [97] => index [98] => infile [99] => inner [100] => inout [101] => insensitive [102] => insert [103] => int [104] => int1 [105] => int2 [106] => int3 [107] => int4 [108] => int8 [109] => integer [110] => intersect [111] => interval [112] => into [113] => io_after_gtids [114] => io_before_gtids [115] => is [116] => iterate [117] => join [118] => json_table [119] => key [120] => keys [121] => kill [122] => lag [123] => last_value [124] => lateral [125] => lead [126] => leading [127] => leave [128] => left [129] => like [130] => limit [131] => linear [132] => lines [133] => load [134] => localtime [135] => localtimestamp [136] => lock [137] => long [138] => longblob [139] => longtext [140] => loop [141] => low_priority [142] => master_bind [143] => master_ssl_verify_server_cert [144] => match [145] => maxvalue [146] => mediumblob [147] => mediumint [148] => mediumtext [149] => middleint [150] => minute_microsecond [151] => minute_second [152] => mod [153] => modifies [154] => natural [155] => not [156] => no_write_to_binlog [157] => nth_value [158] => ntile [159] => null [160] => numeric [161] => of [162] => on [163] => optimize [164] => optimizer_costs [165] => option [166] => optionally [167] => or [168] => order [169] => out [170] => outer [171] => outfile [172] => over [173] => partition [174] => percent_rank [175] => persist [176] => persist_only [177] => precision [178] => primary [179] => procedure [180] => purge [181] => range [182] => rank [183] => read [184] => reads [185] => read_write [186] => real [187] => recursive [188] => references [189] => regexp [190] => release [191] => rename [192] => repeat [193] => replace [194] => require [195] => resignal [196] => restrict [197] => return [198] => revoke [199] => right [200] => rlike [201] => row [202] => rows [203] => row_number [204] => schema [205] => schemas [206] => second_microsecond [207] => select [208] => sensitive [209] => separator [210] => set [211] => show [212] => signal [213] => smallint [214] => spatial [215] => specific [216] => sql [217] => sqlexception [218] => sqlstate [219] => sqlwarning [220] => sql_big_result [221] => sql_calc_found_rows [222] => sql_small_result [223] => ssl [224] => starting [225] => stored [226] => straight_join [227] => system [228] => table [229] => terminated [230] => then [231] => tinyblob [232] => tinyint [233] => tinytext [234] => to [235] => trailing [236] => trigger [237] => true [238] => undo [239] => union [240] => unique [241] => unlock [242] => unsigned [243] => update [244] => usage [245] => use [246] => using [247] => utc_date [248] => utc_time [249] => utc_timestamp [250] => values [251] => varbinary [252] => varchar [253] => varcharacter [254] => varying [255] => virtual [256] => when [257] => where [258] => while [259] => window [260] => with [261] => write [262] => xor [263] => year_month [264] => zerofill ) ) [type] => -> [args] => Array ( [0] => Array ( [driver] => mysql [database] => hitrahr [username] => root [password] => asdf [host] => localhost [prefix] => Array ( [default] => ) [pdo] => Array ( [1000] => 1 [20] => 1 [17] => 1 [1013] => ) ) ) ) [5] => Array ( [file] => /apps/hitra7/drupal7/includes/database/database.inc [line] => 1582 [function] => openConnection [class] => Database [type] => :: [args] => Array ( [0] => hitrahr [1] => default ) ) [6] => Array ( [file] => /apps/hitra7/drupal7/includes/database/database.inc [line] => 2467 [function] => getConnection [class] => Database [type] => :: [args] => Array ( [0] => default ) ) [7] => Array ( [file] => /apps/nobleprog-website/includes/functions/new-modules-general-functions.php [line] => 31 [function] => db_query [args] => Array ( [0] => SELECT entity_id FROM field_data_field_url_alias WHERE field_url_alias_value = :alias AND entity_type = 'taxonomy_term' AND language = :language [1] => Array ( [:alias] => cc [:language] => en ) ) ) [8] => Array ( [file] => /apps/nobleprog-website/includes/functions/category-functions.php [line] => 149 [function] => np_db_query [args] => Array ( [0] => hitrahr [1] => db_query [2] => SELECT entity_id FROM field_data_field_url_alias WHERE field_url_alias_value = :alias AND entity_type = 'taxonomy_term' AND language = :language [3] => Array ( [:alias] => cc [:language] => en ) ) ) [9] => Array ( [file] => /apps/nobleprog-website/routes.logic.php [line] => 75 [function] => category_validate_url_alias [args] => Array ( [0] => cc ) ) [10] => Array ( [file] => /apps/nobleprog-website/__index.php [line] => 86 [function] => check_for_module [args] => Array ( [0] => /en/cc/mlbigdata [1] => Array ( [0] => [1] => cc [2] => mlbigdata [3] => en ) ) ) [11] => Array ( [file] => /apps/nobleprog-website/_index.php [line] => 26 [args] => Array ( [0] => /apps/nobleprog-website/__index.php ) [function] => include_once ) [12] => Array ( [file] => /apps/hitra7/index.php [line] => 54 [args] => Array ( [0] => /apps/nobleprog-website/_index.php ) [function] => include_once ) ) NP URI: www.nobleprog.com.ec/en/cc/mlbigdata Undefined array key "mysql_identifier_quote_character" /apps/nobleprog-website/includes/functions/new-modules-general-functions.php:82 Array ( [0] => Array ( [file] => /apps/nobleprog-website/includes/functions/new-modules-general-functions.php [line] => 82 [function] => myErrorHandler [args] => Array ( [0] => 2 [1] => Undefined array key "mysql_identifier_quote_character" [2] => /apps/nobleprog-website/includes/functions/new-modules-general-functions.php [3] => 82 ) ) [1] => Array ( [file] => /apps/hitra7/drupal7/includes/database/mysql/database.inc [line] => 397 [function] => variable_get [args] => Array ( [0] => mysql_identifier_quote_character [1] => ` ) ) [2] => Array ( [file] => /apps/hitra7/drupal7/includes/database/database.inc [line] => 329 [function] => setPrefix [class] => DatabaseConnection_mysql [object] => DatabaseConnection_mysql Object ( [target:protected] => [key:protected] => [logger:protected] => [transactionLayers:protected] => Array ( ) [driverClasses:protected] => Array ( ) [statementClass:protected] => DatabaseStatementBase [transactionSupport:protected] => 1 [transactionalDDLSupport:protected] => [temporaryNameIndex:protected] => 0 [connection:protected] => [connectionOptions:protected] => Array ( [driver] => mysql [database] => common_fe [username] => root [password] => asdf [host] => localhost [prefix] => Array ( [default] => ) ) [schema:protected] => [prefixes:protected] => Array ( [default] => ) [prefixSearch:protected] => Array ( [0] => { [1] => } ) [prefixReplace:protected] => Array ( [0] => [1] => ) [escapedNames:protected] => Array ( ) [escapedAliases:protected] => Array ( ) [unprefixedTablesMap:protected] => Array ( ) [needsCleanup:protected] => [reservedKeyWords:DatabaseConnection_mysql:private] => Array ( [0] => accessible [1] => add [2] => admin [3] => all [4] => alter [5] => analyze [6] => and [7] => as [8] => asc [9] => asensitive [10] => before [11] => between [12] => bigint [13] => binary [14] => blob [15] => both [16] => by [17] => call [18] => cascade [19] => case [20] => change [21] => char [22] => character [23] => check [24] => collate [25] => column [26] => condition [27] => constraint [28] => continue [29] => convert [30] => create [31] => cross [32] => cube [33] => cume_dist [34] => current_date [35] => current_time [36] => current_timestamp [37] => current_user [38] => cursor [39] => database [40] => databases [41] => day_hour [42] => day_microsecond [43] => day_minute [44] => day_second [45] => dec [46] => decimal [47] => declare [48] => default [49] => delayed [50] => delete [51] => dense_rank [52] => desc [53] => describe [54] => deterministic [55] => distinct [56] => distinctrow [57] => div [58] => double [59] => drop [60] => dual [61] => each [62] => else [63] => elseif [64] => empty [65] => enclosed [66] => escaped [67] => except [68] => exists [69] => exit [70] => explain [71] => false [72] => fetch [73] => first_value [74] => float [75] => float4 [76] => float8 [77] => for [78] => force [79] => foreign [80] => from [81] => fulltext [82] => function [83] => generated [84] => get [85] => grant [86] => group [87] => grouping [88] => groups [89] => having [90] => high_priority [91] => hour_microsecond [92] => hour_minute [93] => hour_second [94] => if [95] => ignore [96] => in [97] => index [98] => infile [99] => inner [100] => inout [101] => insensitive [102] => insert [103] => int [104] => int1 [105] => int2 [106] => int3 [107] => int4 [108] => int8 [109] => integer [110] => intersect [111] => interval [112] => into [113] => io_after_gtids [114] => io_before_gtids [115] => is [116] => iterate [117] => join [118] => json_table [119] => key [120] => keys [121] => kill [122] => lag [123] => last_value [124] => lateral [125] => lead [126] => leading [127] => leave [128] => left [129] => like [130] => limit [131] => linear [132] => lines [133] => load [134] => localtime [135] => localtimestamp [136] => lock [137] => long [138] => longblob [139] => longtext [140] => loop [141] => low_priority [142] => master_bind [143] => master_ssl_verify_server_cert [144] => match [145] => maxvalue [146] => mediumblob [147] => mediumint [148] => mediumtext [149] => middleint [150] => minute_microsecond [151] => minute_second [152] => mod [153] => modifies [154] => natural [155] => not [156] => no_write_to_binlog [157] => nth_value [158] => ntile [159] => null [160] => numeric [161] => of [162] => on [163] => optimize [164] => optimizer_costs [165] => option [166] => optionally [167] => or [168] => order [169] => out [170] => outer [171] => outfile [172] => over [173] => partition [174] => percent_rank [175] => persist [176] => persist_only [177] => precision [178] => primary [179] => procedure [180] => purge [181] => range [182] => rank [183] => read [184] => reads [185] => read_write [186] => real [187] => recursive [188] => references [189] => regexp [190] => release [191] => rename [192] => repeat [193] => replace [194] => require [195] => resignal [196] => restrict [197] => return [198] => revoke [199] => right [200] => rlike [201] => row [202] => rows [203] => row_number [204] => schema [205] => schemas [206] => second_microsecond [207] => select [208] => sensitive [209] => separator [210] => set [211] => show [212] => signal [213] => smallint [214] => spatial [215] => specific [216] => sql [217] => sqlexception [218] => sqlstate [219] => sqlwarning [220] => sql_big_result [221] => sql_calc_found_rows [222] => sql_small_result [223] => ssl [224] => starting [225] => stored [226] => straight_join [227] => system [228] => table [229] => terminated [230] => then [231] => tinyblob [232] => tinyint [233] => tinytext [234] => to [235] => trailing [236] => trigger [237] => true [238] => undo [239] => union [240] => unique [241] => unlock [242] => unsigned [243] => update [244] => usage [245] => use [246] => using [247] => utc_date [248] => utc_time [249] => utc_timestamp [250] => values [251] => varbinary [252] => varchar [253] => varcharacter [254] => varying [255] => virtual [256] => when [257] => where [258] => while [259] => window [260] => with [261] => write [262] => xor [263] => year_month [264] => zerofill ) ) [type] => -> [args] => Array ( [0] => Array ( [default] => ) ) ) [3] => Array ( [file] => /apps/hitra7/drupal7/includes/database/mysql/database.inc [line] => 349 [function] => __construct [class] => DatabaseConnection [object] => DatabaseConnection_mysql Object ( [target:protected] => [key:protected] => [logger:protected] => [transactionLayers:protected] => Array ( ) [driverClasses:protected] => Array ( ) [statementClass:protected] => DatabaseStatementBase [transactionSupport:protected] => 1 [transactionalDDLSupport:protected] => [temporaryNameIndex:protected] => 0 [connection:protected] => [connectionOptions:protected] => Array ( [driver] => mysql [database] => common_fe [username] => root [password] => asdf [host] => localhost [prefix] => Array ( [default] => ) ) [schema:protected] => [prefixes:protected] => Array ( [default] => ) [prefixSearch:protected] => Array ( [0] => { [1] => } ) [prefixReplace:protected] => Array ( [0] => [1] => ) [escapedNames:protected] => Array ( ) [escapedAliases:protected] => Array ( ) [unprefixedTablesMap:protected] => Array ( ) [needsCleanup:protected] => [reservedKeyWords:DatabaseConnection_mysql:private] => Array ( [0] => accessible [1] => add [2] => admin [3] => all [4] => alter [5] => analyze [6] => and [7] => as [8] => asc [9] => asensitive [10] => before [11] => between [12] => bigint [13] => binary [14] => blob [15] => both [16] => by [17] => call [18] => cascade [19] => case [20] => change [21] => char [22] => character [23] => check [24] => collate [25] => column [26] => condition [27] => constraint [28] => continue [29] => convert [30] => create [31] => cross [32] => cube [33] => cume_dist [34] => current_date [35] => current_time [36] => current_timestamp [37] => current_user [38] => cursor [39] => database [40] => databases [41] => day_hour [42] => day_microsecond [43] => day_minute [44] => day_second [45] => dec [46] => decimal [47] => declare [48] => default [49] => delayed [50] => delete [51] => dense_rank [52] => desc [53] => describe [54] => deterministic [55] => distinct [56] => distinctrow [57] => div [58] => double [59] => drop [60] => dual [61] => each [62] => else [63] => elseif [64] => empty [65] => enclosed [66] => escaped [67] => except [68] => exists [69] => exit [70] => explain [71] => false [72] => fetch [73] => first_value [74] => float [75] => float4 [76] => float8 [77] => for [78] => force [79] => foreign [80] => from [81] => fulltext [82] => function [83] => generated [84] => get [85] => grant [86] => group [87] => grouping [88] => groups [89] => having [90] => high_priority [91] => hour_microsecond [92] => hour_minute [93] => hour_second [94] => if [95] => ignore [96] => in [97] => index [98] => infile [99] => inner [100] => inout [101] => insensitive [102] => insert [103] => int [104] => int1 [105] => int2 [106] => int3 [107] => int4 [108] => int8 [109] => integer [110] => intersect [111] => interval [112] => into [113] => io_after_gtids [114] => io_before_gtids [115] => is [116] => iterate [117] => join [118] => json_table [119] => key [120] => keys [121] => kill [122] => lag [123] => last_value [124] => lateral [125] => lead [126] => leading [127] => leave [128] => left [129] => like [130] => limit [131] => linear [132] => lines [133] => load [134] => localtime [135] => localtimestamp [136] => lock [137] => long [138] => longblob [139] => longtext [140] => loop [141] => low_priority [142] => master_bind [143] => master_ssl_verify_server_cert [144] => match [145] => maxvalue [146] => mediumblob [147] => mediumint [148] => mediumtext [149] => middleint [150] => minute_microsecond [151] => minute_second [152] => mod [153] => modifies [154] => natural [155] => not [156] => no_write_to_binlog [157] => nth_value [158] => ntile [159] => null [160] => numeric [161] => of [162] => on [163] => optimize [164] => optimizer_costs [165] => option [166] => optionally [167] => or [168] => order [169] => out [170] => outer [171] => outfile [172] => over [173] => partition [174] => percent_rank [175] => persist [176] => persist_only [177] => precision [178] => primary [179] => procedure [180] => purge [181] => range [182] => rank [183] => read [184] => reads [185] => read_write [186] => real [187] => recursive [188] => references [189] => regexp [190] => release [191] => rename [192] => repeat [193] => replace [194] => require [195] => resignal [196] => restrict [197] => return [198] => revoke [199] => right [200] => rlike [201] => row [202] => rows [203] => row_number [204] => schema [205] => schemas [206] => second_microsecond [207] => select [208] => sensitive [209] => separator [210] => set [211] => show [212] => signal [213] => smallint [214] => spatial [215] => specific [216] => sql [217] => sqlexception [218] => sqlstate [219] => sqlwarning [220] => sql_big_result [221] => sql_calc_found_rows [222] => sql_small_result [223] => ssl [224] => starting [225] => stored [226] => straight_join [227] => system [228] => table [229] => terminated [230] => then [231] => tinyblob [232] => tinyint [233] => tinytext [234] => to [235] => trailing [236] => trigger [237] => true [238] => undo [239] => union [240] => unique [241] => unlock [242] => unsigned [243] => update [244] => usage [245] => use [246] => using [247] => utc_date [248] => utc_time [249] => utc_timestamp [250] => values [251] => varbinary [252] => varchar [253] => varcharacter [254] => varying [255] => virtual [256] => when [257] => where [258] => while [259] => window [260] => with [261] => write [262] => xor [263] => year_month [264] => zerofill ) ) [type] => -> [args] => Array ( [0] => mysql:host=localhost;port=3306;charset=utf8;dbname=common_fe [1] => root [2] => asdf [3] => Array ( [1000] => 1 [20] => 1 [17] => 1 [1013] => ) ) ) [4] => Array ( [file] => /apps/hitra7/drupal7/includes/database/database.inc [line] => 1796 [function] => __construct [class] => DatabaseConnection_mysql [object] => DatabaseConnection_mysql Object ( [target:protected] => [key:protected] => [logger:protected] => [transactionLayers:protected] => Array ( ) [driverClasses:protected] => Array ( ) [statementClass:protected] => DatabaseStatementBase [transactionSupport:protected] => 1 [transactionalDDLSupport:protected] => [temporaryNameIndex:protected] => 0 [connection:protected] => [connectionOptions:protected] => Array ( [driver] => mysql [database] => common_fe [username] => root [password] => asdf [host] => localhost [prefix] => Array ( [default] => ) ) [schema:protected] => [prefixes:protected] => Array ( [default] => ) [prefixSearch:protected] => Array ( [0] => { [1] => } ) [prefixReplace:protected] => Array ( [0] => [1] => ) [escapedNames:protected] => Array ( ) [escapedAliases:protected] => Array ( ) [unprefixedTablesMap:protected] => Array ( ) [needsCleanup:protected] => [reservedKeyWords:DatabaseConnection_mysql:private] => Array ( [0] => accessible [1] => add [2] => admin [3] => all [4] => alter [5] => analyze [6] => and [7] => as [8] => asc [9] => asensitive [10] => before [11] => between [12] => bigint [13] => binary [14] => blob [15] => both [16] => by [17] => call [18] => cascade [19] => case [20] => change [21] => char [22] => character [23] => check [24] => collate [25] => column [26] => condition [27] => constraint [28] => continue [29] => convert [30] => create [31] => cross [32] => cube [33] => cume_dist [34] => current_date [35] => current_time [36] => current_timestamp [37] => current_user [38] => cursor [39] => database [40] => databases [41] => day_hour [42] => day_microsecond [43] => day_minute [44] => day_second [45] => dec [46] => decimal [47] => declare [48] => default [49] => delayed [50] => delete [51] => dense_rank [52] => desc [53] => describe [54] => deterministic [55] => distinct [56] => distinctrow [57] => div [58] => double [59] => drop [60] => dual [61] => each [62] => else [63] => elseif [64] => empty [65] => enclosed [66] => escaped [67] => except [68] => exists [69] => exit [70] => explain [71] => false [72] => fetch [73] => first_value [74] => float [75] => float4 [76] => float8 [77] => for [78] => force [79] => foreign [80] => from [81] => fulltext [82] => function [83] => generated [84] => get [85] => grant [86] => group [87] => grouping [88] => groups [89] => having [90] => high_priority [91] => hour_microsecond [92] => hour_minute [93] => hour_second [94] => if [95] => ignore [96] => in [97] => index [98] => infile [99] => inner [100] => inout [101] => insensitive [102] => insert [103] => int [104] => int1 [105] => int2 [106] => int3 [107] => int4 [108] => int8 [109] => integer [110] => intersect [111] => interval [112] => into [113] => io_after_gtids [114] => io_before_gtids [115] => is [116] => iterate [117] => join [118] => json_table [119] => key [120] => keys [121] => kill [122] => lag [123] => last_value [124] => lateral [125] => lead [126] => leading [127] => leave [128] => left [129] => like [130] => limit [131] => linear [132] => lines [133] => load [134] => localtime [135] => localtimestamp [136] => lock [137] => long [138] => longblob [139] => longtext [140] => loop [141] => low_priority [142] => master_bind [143] => master_ssl_verify_server_cert [144] => match [145] => maxvalue [146] => mediumblob [147] => mediumint [148] => mediumtext [149] => middleint [150] => minute_microsecond [151] => minute_second [152] => mod [153] => modifies [154] => natural [155] => not [156] => no_write_to_binlog [157] => nth_value [158] => ntile [159] => null [160] => numeric [161] => of [162] => on [163] => optimize [164] => optimizer_costs [165] => option [166] => optionally [167] => or [168] => order [169] => out [170] => outer [171] => outfile [172] => over [173] => partition [174] => percent_rank [175] => persist [176] => persist_only [177] => precision [178] => primary [179] => procedure [180] => purge [181] => range [182] => rank [183] => read [184] => reads [185] => read_write [186] => real [187] => recursive [188] => references [189] => regexp [190] => release [191] => rename [192] => repeat [193] => replace [194] => require [195] => resignal [196] => restrict [197] => return [198] => revoke [199] => right [200] => rlike [201] => row [202] => rows [203] => row_number [204] => schema [205] => schemas [206] => second_microsecond [207] => select [208] => sensitive [209] => separator [210] => set [211] => show [212] => signal [213] => smallint [214] => spatial [215] => specific [216] => sql [217] => sqlexception [218] => sqlstate [219] => sqlwarning [220] => sql_big_result [221] => sql_calc_found_rows [222] => sql_small_result [223] => ssl [224] => starting [225] => stored [226] => straight_join [227] => system [228] => table [229] => terminated [230] => then [231] => tinyblob [232] => tinyint [233] => tinytext [234] => to [235] => trailing [236] => trigger [237] => true [238] => undo [239] => union [240] => unique [241] => unlock [242] => unsigned [243] => update [244] => usage [245] => use [246] => using [247] => utc_date [248] => utc_time [249] => utc_timestamp [250] => values [251] => varbinary [252] => varchar [253] => varcharacter [254] => varying [255] => virtual [256] => when [257] => where [258] => while [259] => window [260] => with [261] => write [262] => xor [263] => year_month [264] => zerofill ) ) [type] => -> [args] => Array ( [0] => Array ( [driver] => mysql [database] => common_fe [username] => root [password] => asdf [host] => localhost [prefix] => Array ( [default] => ) [pdo] => Array ( [1000] => 1 [20] => 1 [17] => 1 [1013] => ) ) ) ) [5] => Array ( [file] => /apps/hitra7/drupal7/includes/database/database.inc [line] => 1582 [function] => openConnection [class] => Database [type] => :: [args] => Array ( [0] => common_fe [1] => default ) ) [6] => Array ( [file] => /apps/hitra7/drupal7/includes/database/database.inc [line] => 2467 [function] => getConnection [class] => Database [type] => :: [args] => Array ( [0] => default ) ) [7] => Array ( [file] => /apps/nobleprog-website/includes/functions/new-modules-general-functions.php [line] => 31 [function] => db_query [args] => Array ( [0] => SELECT * FROM price_formulas WHERE country_code = :country_code [1] => Array ( [:country_code] => ec ) ) ) [8] => Array ( [file] => /apps/nobleprog-website/includes/functions/course-prices.php [line] => 111 [function] => np_db_query [args] => Array ( [0] => common_fe [1] => db_query [2] => SELECT * FROM price_formulas WHERE country_code = :country_code [3] => Array ( [:country_code] => ec ) ) ) [9] => Array ( [file] => /apps/nobleprog-website/includes/functions/course-prices.php [line] => 93 [function] => get_formula [args] => Array ( [0] => ec ) ) [10] => Array ( [file] => /apps/nobleprog-website/includes/functions/course-prices.php [line] => 355 [function] => course_price_v2_formula [args] => Array ( ) ) [11] => Array ( [file] => /apps/nobleprog-website/includes/functions/course-prices.php [line] => 344 [function] => course_price_change_to_fe_p [args] => Array ( [0] => mlbigdata [1] => 7 [2] => za_premium,uk_premium,pl_2500 [3] => 0 [4] => [5] => USD ) ) [12] => Array ( [file] => /apps/nobleprog-website/includes/functions/course-prices.php [line] => 316 [function] => course_price_get_default_price [args] => Array ( [0] => mlbigdata ) ) [13] => Array ( [file] => /apps/nobleprog-website/includes/functions/course-prices.php [line] => 15 [function] => course_price_get_price [args] => Array ( [0] => mlbigdata ) ) [14] => Array ( [file] => /apps/nobleprog-website/modules/course/course.php [line] => 23 [function] => course_price_virtual_event_price [args] => Array ( [0] => mlbigdata ) ) [15] => Array ( [file] => /apps/nobleprog-website/core/routes.php [line] => 19 [function] => course_menu_callback [args] => Array ( [0] => /en/cc/mlbigdata ) ) [16] => Array ( [file] => /apps/nobleprog-website/__index.php [line] => 100 [args] => Array ( [0] => /apps/nobleprog-website/core/routes.php ) [function] => require_once ) [17] => Array ( [file] => /apps/nobleprog-website/_index.php [line] => 26 [args] => Array ( [0] => /apps/nobleprog-website/__index.php ) [function] => include_once ) [18] => Array ( [file] => /apps/hitra7/index.php [line] => 54 [args] => Array ( [0] => /apps/nobleprog-website/_index.php ) [function] => include_once ) ) NP URI: www.nobleprog.com.ec/en/cc/mlbigdata Undefined array key "sdp" /apps/nobleprog-website/includes/functions/course-prices.php:281 Array ( [0] => Array ( [file] => /apps/nobleprog-website/includes/functions/course-prices.php [line] => 281 [function] => myErrorHandler [args] => Array ( [0] => 2 [1] => Undefined array key "sdp" [2] => /apps/nobleprog-website/includes/functions/course-prices.php [3] => 281 ) ) [1] => Array ( [file] => /apps/nobleprog-website/includes/functions/course-prices.php [line] => 45 [function] => course_price_table [args] => Array ( [0] => Array ( [fdp] => 2312 [adp] => 687 [reduced_fdp] => [reduced_adp] => [days] => 1 [default_venue_fdc] => 350 [default_venue_adc] => 50 [people] => 1 [hours] => 7 [course_code] => mlbigdata [venue_id] => ec_15661446 [vfdc] => 175.00 [vadc] => 60.00 ) [1] => 10 ) ) [2] => Array ( [file] => /apps/nobleprog-website/modules/course/course.php [line] => 23 [function] => course_price_virtual_event_price [args] => Array ( [0] => mlbigdata ) ) [3] => Array ( [file] => /apps/nobleprog-website/core/routes.php [line] => 19 [function] => course_menu_callback [args] => Array ( [0] => /en/cc/mlbigdata ) ) [4] => Array ( [file] => /apps/nobleprog-website/__index.php [line] => 100 [args] => Array ( [0] => /apps/nobleprog-website/core/routes.php ) [function] => require_once ) [5] => Array ( [file] => /apps/nobleprog-website/_index.php [line] => 26 [args] => Array ( [0] => /apps/nobleprog-website/__index.php ) [function] => include_once ) [6] => Array ( [file] => /apps/hitra7/index.php [line] => 54 [args] => Array ( [0] => /apps/nobleprog-website/_index.php ) [function] => include_once ) ) NP URI: www.nobleprog.com.ec/en/cc/mlbigdata Undefined array key "nobleprog_default_trainer_journey" /apps/nobleprog-website/includes/functions/course-prices.php:286 Array ( [0] => Array ( [file] => /apps/nobleprog-website/includes/functions/course-prices.php [line] => 286 [function] => myErrorHandler [args] => Array ( [0] => 2 [1] => Undefined array key "nobleprog_default_trainer_journey" [2] => /apps/nobleprog-website/includes/functions/course-prices.php [3] => 286 ) ) [1] => Array ( [file] => /apps/nobleprog-website/includes/functions/course-prices.php [line] => 45 [function] => course_price_table [args] => Array ( [0] => Array ( [fdp] => 2312 [adp] => 687 [reduced_fdp] => [reduced_adp] => [days] => 1 [default_venue_fdc] => 350 [default_venue_adc] => 50 [people] => 1 [hours] => 7 [course_code] => mlbigdata [venue_id] => ec_15661446 [vfdc] => 175.00 [vadc] => 60.00 ) [1] => 10 ) ) [2] => Array ( [file] => /apps/nobleprog-website/modules/course/course.php [line] => 23 [function] => course_price_virtual_event_price [args] => Array ( [0] => mlbigdata ) ) [3] => Array ( [file] => /apps/nobleprog-website/core/routes.php [line] => 19 [function] => course_menu_callback [args] => Array ( [0] => /en/cc/mlbigdata ) ) [4] => Array ( [file] => /apps/nobleprog-website/__index.php [line] => 100 [args] => Array ( [0] => /apps/nobleprog-website/core/routes.php ) [function] => require_once ) [5] => Array ( [file] => /apps/nobleprog-website/_index.php [line] => 26 [args] => Array ( [0] => /apps/nobleprog-website/__index.php ) [function] => include_once ) [6] => Array ( [file] => /apps/hitra7/index.php [line] => 54 [args] => Array ( [0] => /apps/nobleprog-website/_index.php ) [function] => include_once ) ) NP URI: www.nobleprog.com.ec/en/cc/mlbigdata Undefined array key "nobleprog_price_rounding" /apps/nobleprog-website/includes/functions/course-prices.php:289 Array ( [0] => Array ( [file] => /apps/nobleprog-website/includes/functions/course-prices.php [line] => 289 [function] => myErrorHandler [args] => Array ( [0] => 2 [1] => Undefined array key "nobleprog_price_rounding" [2] => /apps/nobleprog-website/includes/functions/course-prices.php [3] => 289 ) ) [1] => Array ( [file] => /apps/nobleprog-website/includes/functions/course-prices.php [line] => 45 [function] => course_price_table [args] => Array ( [0] => Array ( [fdp] => 2312 [adp] => 687 [reduced_fdp] => [reduced_adp] => [days] => 1 [default_venue_fdc] => 350 [default_venue_adc] => 50 [people] => 1 [hours] => 7 [course_code] => mlbigdata [venue_id] => ec_15661446 [vfdc] => 175.00 [vadc] => 60.00 ) [1] => 10 ) ) [2] => Array ( [file] => /apps/nobleprog-website/modules/course/course.php [line] => 23 [function] => course_price_virtual_event_price [args] => Array ( [0] => mlbigdata ) ) [3] => Array ( [file] => /apps/nobleprog-website/core/routes.php [line] => 19 [function] => course_menu_callback [args] => Array ( [0] => /en/cc/mlbigdata ) ) [4] => Array ( [file] => /apps/nobleprog-website/__index.php [line] => 100 [args] => Array ( [0] => /apps/nobleprog-website/core/routes.php ) [function] => require_once ) [5] => Array ( [file] => /apps/nobleprog-website/_index.php [line] => 26 [args] => Array ( [0] => /apps/nobleprog-website/__index.php ) [function] => include_once ) [6] => Array ( [file] => /apps/hitra7/index.php [line] => 54 [args] => Array ( [0] => /apps/nobleprog-website/_index.php ) [function] => include_once ) ) NP URI: www.nobleprog.com.ec/en/cc/mlbigdata Undefined array key "sdp" /apps/nobleprog-website/includes/functions/course-prices.php:281 Array ( [0] => Array ( [file] => /apps/nobleprog-website/includes/functions/course-prices.php [line] => 281 [function] => myErrorHandler [args] => Array ( [0] => 2 [1] => Undefined array key "sdp" [2] => /apps/nobleprog-website/includes/functions/course-prices.php [3] => 281 ) ) [1] => Array ( [file] => /apps/nobleprog-website/includes/functions/course-prices.php [line] => 45 [function] => course_price_table [args] => Array ( [0] => Array ( [fdp] => 2312 [adp] => 687 [reduced_fdp] => [reduced_adp] => [days] => 1 [default_venue_fdc] => 350 [default_venue_adc] => 50 [people] => 1 [hours] => 7 [course_code] => mlbigdata [venue_id] => ec_15661447 [vfdc] => 200.00 [vadc] => 50.00 ) [1] => 10 ) ) [2] => Array ( [file] => /apps/nobleprog-website/modules/course/course.php [line] => 23 [function] => course_price_virtual_event_price [args] => Array ( [0] => mlbigdata ) ) [3] => Array ( [file] => /apps/nobleprog-website/core/routes.php [line] => 19 [function] => course_menu_callback [args] => Array ( [0] => /en/cc/mlbigdata ) ) [4] => Array ( [file] => /apps/nobleprog-website/__index.php [line] => 100 [args] => Array ( [0] => /apps/nobleprog-website/core/routes.php ) [function] => require_once ) [5] => Array ( [file] => /apps/nobleprog-website/_index.php [line] => 26 [args] => Array ( [0] => /apps/nobleprog-website/__index.php ) [function] => include_once ) [6] => Array ( [file] => /apps/hitra7/index.php [line] => 54 [args] => Array ( [0] => /apps/nobleprog-website/_index.php ) [function] => include_once ) ) NP URI: www.nobleprog.com.ec/en/cc/mlbigdata Undefined array key "nobleprog_default_trainer_journey" /apps/nobleprog-website/includes/functions/course-prices.php:286 Array ( [0] => Array ( [file] => /apps/nobleprog-website/includes/functions/course-prices.php [line] => 286 [function] => myErrorHandler [args] => Array ( [0] => 2 [1] => Undefined array key "nobleprog_default_trainer_journey" [2] => /apps/nobleprog-website/includes/functions/course-prices.php [3] => 286 ) ) [1] => Array ( [file] => /apps/nobleprog-website/includes/functions/course-prices.php [line] => 45 [function] => course_price_table [args] => Array ( [0] => Array ( [fdp] => 2312 [adp] => 687 [reduced_fdp] => [reduced_adp] => [days] => 1 [default_venue_fdc] => 350 [default_venue_adc] => 50 [people] => 1 [hours] => 7 [course_code] => mlbigdata [venue_id] => ec_15661447 [vfdc] => 200.00 [vadc] => 50.00 ) [1] => 10 ) ) [2] => Array ( [file] => /apps/nobleprog-website/modules/course/course.php [line] => 23 [function] => course_price_virtual_event_price [args] => Array ( [0] => mlbigdata ) ) [3] => Array ( [file] => /apps/nobleprog-website/core/routes.php [line] => 19 [function] => course_menu_callback [args] => Array ( [0] => /en/cc/mlbigdata ) ) [4] => Array ( [file] => /apps/nobleprog-website/__index.php [line] => 100 [args] => Array ( [0] => /apps/nobleprog-website/core/routes.php ) [function] => require_once ) [5] => Array ( [file] => /apps/nobleprog-website/_index.php [line] => 26 [args] => Array ( [0] => /apps/nobleprog-website/__index.php ) [function] => include_once ) [6] => Array ( [file] => /apps/hitra7/index.php [line] => 54 [args] => Array ( [0] => /apps/nobleprog-website/_index.php ) [function] => include_once ) ) NP URI: www.nobleprog.com.ec/en/cc/mlbigdata Undefined array key "nobleprog_price_rounding" /apps/nobleprog-website/includes/functions/course-prices.php:289 Array ( [0] => Array ( [file] => /apps/nobleprog-website/includes/functions/course-prices.php [line] => 289 [function] => myErrorHandler [args] => Array ( [0] => 2 [1] => Undefined array key "nobleprog_price_rounding" [2] => /apps/nobleprog-website/includes/functions/course-prices.php [3] => 289 ) ) [1] => Array ( [file] => /apps/nobleprog-website/includes/functions/course-prices.php [line] => 45 [function] => course_price_table [args] => Array ( [0] => Array ( [fdp] => 2312 [adp] => 687 [reduced_fdp] => [reduced_adp] => [days] => 1 [default_venue_fdc] => 350 [default_venue_adc] => 50 [people] => 1 [hours] => 7 [course_code] => mlbigdata [venue_id] => ec_15661447 [vfdc] => 200.00 [vadc] => 50.00 ) [1] => 10 ) ) [2] => Array ( [file] => /apps/nobleprog-website/modules/course/course.php [line] => 23 [function] => course_price_virtual_event_price [args] => Array ( [0] => mlbigdata ) ) [3] => Array ( [file] => /apps/nobleprog-website/core/routes.php [line] => 19 [function] => course_menu_callback [args] => Array ( [0] => /en/cc/mlbigdata ) ) [4] => Array ( [file] => /apps/nobleprog-website/__index.php [line] => 100 [args] => Array ( [0] => /apps/nobleprog-website/core/routes.php ) [function] => require_once ) [5] => Array ( [file] => /apps/nobleprog-website/_index.php [line] => 26 [args] => Array ( [0] => /apps/nobleprog-website/__index.php ) [function] => include_once ) [6] => Array ( [file] => /apps/hitra7/index.php [line] => 54 [args] => Array ( [0] => /apps/nobleprog-website/_index.php ) [function] => include_once ) ) NP URI: www.nobleprog.com.ec/en/cc/mlbigdata Cannot modify header information - headers already sent by (output started at /apps/nobleprog-website/_index.php:16) /apps/nobleprog-website/modules/course/course.php:119 Array ( [0] => Array ( [function] => myErrorHandler [args] => Array ( [0] => 2 [1] => Cannot modify header information - headers already sent by (output started at /apps/nobleprog-website/_index.php:16) [2] => /apps/nobleprog-website/modules/course/course.php [3] => 119 ) ) [1] => Array ( [file] => /apps/nobleprog-website/modules/course/course.php [line] => 119 [function] => header [args] => Array ( [0] => X-CSRF-Token:Tm9ibGVQcm9nMTcxNjAyMzk5NQ== ) ) [2] => Array ( [file] => /apps/nobleprog-website/modules/course/course.php [line] => 82 [function] => course_generate_csrf_token [args] => Array ( ) ) [3] => Array ( [file] => /apps/nobleprog-website/modules/course/course.php [line] => 31 [function] => course_render [args] => Array ( [0] => Array ( [course_code] => mlbigdata [hr_nid] => 318287 [title] => Machine Learning and Big Data [requirements] =>

Audience

[overview] =>

This instructor-led, live training (online or onsite) is aimed at technical persons who wish to learn how to implement a machine learning strategy while maximizing the use of big data.

By the end of this training, participants will:

Format of the Course

Course Customization Options

[category_overview] =>

This instructor-led, live training in <loc> (online or onsite) is aimed at technical persons who wish to learn how to implement a machine learning strategy while maximizing the use of big data.

By the end of this training, participants will:

[outline] =>

Introduction

History, Evolution and Trends for Machine Learning

The Role of Big Data in Machine Learning

Infrastructure for Managing Big Data

Using Historical and Real-time Data to Predict Behavior

Case Study: Machine Learning Across Industries

Evaluating Existing Applications and Capabilities

Upskilling for Machine Learning

Tools for Implementing Machine Learning

Cloud vs On-Premise Services

Understanding the Data Middle Backend

Overview of Data Mining and Analysis

Combining Machine Learning with Data Mining

Case Study: Deploying Intelligent Applications to Deliver Personalized Experiences to Users

Summary and Conclusion

[language] => en [duration] => 7 [status] => published [changed] => 1715350267 [source_title] => Machine Learning and Big Data [source_language] => en [cert_code] => [weight] => 0 [excluded_sites] => [use_mt] => stdClass Object ( [field_overview] => [field_course_outline] => [field_prerequisits] => [field_overview_in_category] => ) ) [1] => Array ( [0] => stdClass Object ( [tid] => 766 [alias] => big-data-training [name] => Big Data [english_name] => Big Data [consulting_option] => available_promoted ) [1] => stdClass Object ( [tid] => 943 [alias] => machine-learning-training [name] => Machine Learning [english_name] => Machine Learning [consulting_option] => ) ) [2] => mlbigdata [3] => Array ( [outlines] => Array ( [h2oautoml] => stdClass Object ( [course_code] => h2oautoml [hr_nid] => 306590 [title] => H2O AutoML [requirements] =>

Audience

[overview] =>

H2O AutoML is an artificial intelligence platform that automates the process of building, selecting and optimizing large numbers of machine learning models.

This instructor-led, live training (online or onsite) is aimed at data scientists who wish to use H2O AutoML to automate the process of building and selecting the best machine learning algorithm and parameters.

By the end of this training, participants will be able to:

Format of the Course

Course Customization Options

[category_overview] =>

This instructor-led, live training in <loc> (online or onsite) is aimed at data scientists who wish to use H2O AutoML to automoate the process of building and selecting the best machine learning algorithm and parameters.

By the end of this training, participants will be able to:

[outline] =>

Introduction

Setting up a Working Environment

Installing H2O

Anatomy of a Standard Machine Learning Workflow

Statistical and Machine Learning Algorithms

How H2O Automates the Machine Learning Workflow

Case Study: Predicting Product Availability

Downloading a Dataset

Building a Machine Learning Model

Specify a Training Frame

Training and Cross-Validating Different Models

Tuning the Hyperparameters

Training two Stacked Ensemble Models

Generating a Leaderboard of the Best Models

Inspecting the Ensemble Composition

Training many Deep Neural Network Models

Troubleshooting

Summary and Conclusion

[language] => en [duration] => 14 [status] => published [changed] => 1712161736 [source_title] => H2O AutoML [source_language] => en [cert_code] => [weight] => 0 [excluded_sites] => [use_mt] => stdClass Object ( [field_overview] => [field_course_outline] => [field_prerequisits] => [field_overview_in_category] => ) [cc] => h2oautoml ) [autosklearn] => stdClass Object ( [course_code] => autosklearn [hr_nid] => 306518 [title] => AutoML with Auto-sklearn [requirements] =>

Audience

[overview] =>

Auto-sklearn is a Python package built around the scikit-learn machine learning library. It automatically searches for the right learning algorithm for a new machine learning dataset and optimizes its parameters.

This instructor-led, live training (online or onsite) is aimed at machine learning practitioners who wish to use Auto-sklearn to automate the process of selecting and optimizing a machine learning model.

By the end of this training, participants will be able to:

Format of the Course

Course Customization Options

[category_overview] =>

This instructor-led, live training in <loc> (online or onsite) is aimed at machine learning practitioners who wish to use Auto-sklearn to automate the process of selecting and optimizing a machine learning model.

By the end of this training, participants will be able to:

[outline] =>

Introduction

Setting up a Working Environment

Installing Auto-sklearn

Anatomy of a Standard Machine Learning Workflow

How Auto-sklearn Automates the Machine Learning Workflow

Searching for the Best Neural Network Architecture with NAS (Neural Architecture Search)

Case Study: AutoML with Auto-sklearn

Downloading a Dataset

Building a Machine Learning Model

Training and Testing the Model

Tuning the Hyperparameters

Building, Training, and Testing Additional Models

Tweaking the Hyperparameters to Improve Accuracy

Configuring Auto-sklearn for Deep Learning Models

Troubleshooting

Summary and Conclusion

[language] => en [duration] => 14 [status] => published [changed] => 1712075696 [source_title] => AutoML with Auto-sklearn [source_language] => en [cert_code] => [weight] => 0 [excluded_sites] => [use_mt] => stdClass Object ( [field_overview] => [field_course_outline] => [field_prerequisits] => [field_overview_in_category] => ) [cc] => autosklearn ) [autokeras] => stdClass Object ( [course_code] => autokeras [hr_nid] => 306514 [title] => AutoML with Auto-Keras [requirements] =>

Audience

[overview] =>

Auto-Keras (Also known as Autokeras or Auto Keras) is an open source Python library for automated machine learning (AutoML).

This instructor-led, live training (online or onsite) is aimed at data scientists as well as less technical persons who wish to use Auto-Keras to automate the process of selecting and optimizing a machine learning model.

By the end of this training, participants will be able to:

Format of the Course

Course Customization Options

[category_overview] =>

This instructor-led, live training in <loc> (online or onsite) is aimed at data scientists as well as less technical persons who wish to use Auto-Keras to automate the process of selecting and optimizing a machine learning model.

By the end of this training, participants will be able to:

[outline] =>

Introduction

Setting up a Working Environment

Installing Auto-Keras

Anatomy of a Standard Machine Learning Workflow

How Auto-Keras Automates the Machine Learning Workflow

Searching for the Best Neural Network Architecture with NAS (Neural Architecture Search)

Case Study: AutoML with Auto-Keras

Downloading a Dataset

Building a Machine Learning Model

Training and Testing the Model

Tuning the Hyperparameters

Building, Training, and Testing Additional Models

Tweaking the Hyperparameters to Improve Accuracy

Configuring Auto-Keras for Deep Learning Models

Troubleshooting

Summary and Conclusion

[language] => en [duration] => 14 [status] => published [changed] => 1712161692 [source_title] => AutoML with Auto-Keras [source_language] => en [cert_code] => [weight] => 0 [excluded_sites] => [use_mt] => stdClass Object ( [field_overview] => [field_course_outline] => [field_prerequisits] => [field_overview_in_category] => ) [cc] => autokeras ) [advstablediffusion] => stdClass Object ( [course_code] => advstablediffusion [hr_nid] => 437195 [title] => Advanced Stable Diffusion: Deep Learning for Text-to-Image Generation [requirements] =>

Audience

[overview] =>

Stable Diffusion is a powerful deep learning model that can generate detailed images based on text descriptions. 

This instructor-led, live training (online or onsite) is aimed at intermediate to advanced-level data scientists, machine learning engineers, deep learning researchers, and computer vision experts who wish to expand their knowledge and skills in deep learning for text-to-image generation.

By the end of this training, participants will be able to:

Format of the Course

Course Customization Options

[category_overview] =>

This instructor-led, live training in <loc> (online or onsite) is aimed at intermediate to advanced-level data scientists, machine learning engineers, deep learning researchers, and computer vision experts who wish to expand their knowledge and skills in deep learning for text-to-image generation.

By the end of this training, participants will be able to:

[outline] =>

Introduction to Advanced Stable Diffusion

Advanced Text-to-Image Generation Techniques with Stable Diffusion

Performance Optimization and Scaling for Stable Diffusion

Hyperparameter Tuning and Generalization with Stable Diffusion

Integrating Stable Diffusion with Other Deep Learning Frameworks and Tools

Debugging and Troubleshooting Stable Diffusion Models

Summary and Next Steps

[language] => en [duration] => 21 [status] => published [changed] => 1712162957 [source_title] => Advanced Stable Diffusion: Deep Learning for Text-to-Image Generation [source_language] => en [cert_code] => [weight] => -1002 [excluded_sites] => [use_mt] => stdClass Object ( [field_overview] => [field_course_outline] => [field_prerequisits] => [field_overview_in_category] => ) [cc] => advstablediffusion ) [stablediffusion] => stdClass Object ( [course_code] => stablediffusion [hr_nid] => 437191 [title] => Introduction to Stable Diffusion for Text-to-Image Generation [requirements] =>

Audience

[overview] =>

Stable Diffusion is a powerful deep learning model that can generate detailed images based on text descriptions. 

This instructor-led, live training (online or onsite) is aimed at data scientists, machine learning engineers, and computer vision researchers who wish to leverage Stable Diffusion to generate high-quality images for a variety of use cases.

By the end of this training, participants will be able to:

Format of the Course

Course Customization Options

[category_overview] =>

This instructor-led, live training in (online or onsite) is aimed at data scientists, machine learning engineers, and computer vision researchers who wish to leverage Stable Diffusion to generate high-quality images for a variety of use cases.

By the end of this training, participants will be able to:

[outline] =>

Introduction to Stable Diffusion

Building Stable Diffusion Models

Advanced Stable Diffusion Techniques

Optimizing Stable Diffusion Models

Case Studies and Best Practices

Summary and Next Steps

[language] => en [duration] => 21 [status] => published [changed] => 1712157732 [source_title] => Introduction to Stable Diffusion for Text-to-Image Generation [source_language] => en [cert_code] => [weight] => -1001 [excluded_sites] => [use_mt] => stdClass Object ( [field_overview] => [field_course_outline] => [field_prerequisits] => [field_overview_in_category] => ) [cc] => stablediffusion ) [alphafold] => stdClass Object ( [course_code] => alphafold [hr_nid] => 396067 [title] => AlphaFold [requirements] =>

Audience

[overview] =>

AlphaFold is an Artificial Intelligence (AI) system that performs the prediction of protein structures. It is developed by Alphabet’s/Google’s DeepMind as a deep learning system that can accurately predict 3D models of protein structures.

This instructor-led, live training (online or onsite) is aimed at biologists who wish to understand how AlphaFold works and use AlphaFold models as guides in their experimental studies.

By the end of this training, participants will be able to:

Format of the Course

Course Customization Options

[category_overview] =>

This instructor-led, live training in <loc> (online or onsite) is aimed at biologists who wish to understand how AlphaFold works and use AlphaFold models as guides in their experimental studies.

By the end of this training, participants will be able to:

[outline] =>

Introduction to AlphaFold

How Does AlphaFold Work?

Accessing AlphaFold

AlphaFold Protein Structure Database

AlphaFold Colab

AlphaFold Open Source Code

Interpreting AlphaFold Predictions

AlphaFold Use Cases

Summary and Next Steps

[language] => en [duration] => 7 [status] => published [changed] => 1700037846 [source_title] => AlphaFold [source_language] => en [cert_code] => [weight] => -1001 [excluded_sites] => [use_mt] => stdClass Object ( [field_overview] => [field_course_outline] => [field_prerequisits] => [field_overview_in_category] => ) [cc] => alphafold ) [tensorflowlite] => stdClass Object ( [course_code] => tensorflowlite [hr_nid] => 341007 [title] => TensorFlow Lite for Embedded Linux [requirements] =>

Audience

[overview] =>

TensorFlow Lite is an open source deep learning framework for executing models on mobile and embedded devices with limited compute and memory resources.

This instructor-led, live training (online or onsite) is aimed at developers who wish to use TensorFlow Lite to deploy deep learning models on embedded devices.

By the end of this training, participants will be able to:

Format of the Course

Course Customization Options

[category_overview] =>

This instructor-led, live training in <loc> (online or onsite) is aimed at developers who wish to use TensorFlow Lite to deploy deep learning models on embedded devices.

By the end of this training, participants will be able to:

[outline] =>

Introduction

Overview of TensorFlow Lite Features and Operations

Setting up TensorFlow Lite

Choosing a Model to Run on a Device

Customizing a Pre-trained Model

Converting a Model

Running a Prediction Model

Accelerating Model Operations

Adding Model Operations

Optimizing the Model

Troubleshooting

Summary and Conclusion

[language] => en [duration] => 21 [status] => published [changed] => 1700037625 [source_title] => TensorFlow Lite for Embedded Linux [source_language] => en [cert_code] => [weight] => 0 [excluded_sites] => [use_mt] => stdClass Object ( [field_overview] => [field_course_outline] => [field_prerequisits] => [field_overview_in_category] => ) [cc] => tensorflowlite ) [tensorflowliteandroid] => stdClass Object ( [course_code] => tensorflowliteandroid [hr_nid] => 341059 [title] => TensorFlow Lite for Android [requirements] =>

Audience

[overview] =>

TensorFlow Lite is an open source deep learning framework for mobile devices and embedded systems.

This instructor-led, live training (online or onsite) is aimed at developers who wish to use TensorFlow Lite to develop mobile applications with deep learning capabilities.

By the end of this training, participants will be able to:

Format of the Course

Course Customization Options

[category_overview] =>

This instructor-led, live training in <loc> (online or onsite) is aimed at developers who wish to use TensorFlow Lite to develop mobile applications with deep learning capabilities.

By the end of this training, participants will be able to:

[outline] =>

Introduction

Overview of TensorFlow Lite Features and Design

Machine Learning and Deep Learning Fundamentals

Preparing the Mobile App Development Environment

Creating an App for Object Recognition

Setting up TensorFlow Lite

Selecting a TensorFlow Model

Converting the TensorFlow Model

Loading the TensorFlow Model onto a Mobile Device

Optimizing the TensorFlow Model for Mobile Devices

Adding Chat Capabilities for Smarter Replies

Loading a Pre-trained TensorFlow Model

Retraining a TensorFlow Model

Pre-processing a Dataset

Setting the Hyperparameters

Deploying the AI Enabled App

Running TensorFlow Models on Other Embedded Devices

Troubleshooting

Summary and Conclusion

[language] => en [duration] => 21 [status] => published [changed] => 1700037626 [source_title] => TensorFlow Lite for Android [source_language] => en [cert_code] => [weight] => 0 [excluded_sites] => [use_mt] => stdClass Object ( [field_overview] => [field_course_outline] => [field_prerequisits] => [field_overview_in_category] => ) [cc] => tensorflowliteandroid ) [tensorflowliteios] => stdClass Object ( [course_code] => tensorflowliteios [hr_nid] => 341271 [title] => TensorFlow Lite for iOS [requirements] =>

Audience

[overview] =>

TensorFlow Lite is an open source deep learning framework for mobile devices and embedded systems.

This instructor-led, live training (online or onsite) is aimed at developers who wish to use TensorFlow Lite to develop iOS mobile applications with deep learning capabilities.

By the end of this training, participants will be able to:

Format of the Course

Course Customization Options

[category_overview] =>

This instructor-led, live training in (online or onsite) is aimed at developers who wish to use TensorFlow Lite to develop iOS mobile applications with deep learning capabilities.

By the end of this training, participants will be able to:

[outline] =>

Introduction

Overview of TensorFlow Lite Features and Workflow

Preparing the Development Environment

Capturing an Image with a Device Camera

Creating an App for Object Detection

Creating an App for Image Classification

Customizing the Model and Data

Optimizing the TensorFlow Model

Exploring Alternative Models

Deploying the AI Enabled iOS App

Troubleshooting

Summary and Conclusion

[language] => en [duration] => 21 [status] => published [changed] => 1700037628 [source_title] => TensorFlow Lite for iOS [source_language] => en [cert_code] => [weight] => 0 [excluded_sites] => [use_mt] => stdClass Object ( [field_overview] => [field_course_outline] => [field_prerequisits] => [field_overview_in_category] => ) [cc] => tensorflowliteios ) [tensorflowlitemicro] => stdClass Object ( [course_code] => tensorflowlitemicro [hr_nid] => 341211 [title] => Tensorflow Lite for Microcontrollers [requirements] =>

Audience

[overview] =>

TensorFlow Lite for Microcontrollers is a port of TensorFlow Lite designed to run machine learning models on microcontrollers and other devices with limited memory.

This instructor-led, live training (online or onsite) is aimed at engineers who wish to write, load and run machine learning models on very small embedded devices.

By the end of this training, participants will be able to:

Format of the Course

Course Customization Options

[category_overview] =>

This instructor-led, live training in <loc> (online or onsite) is aimed at engineers who wish to write, load and run machine learning models on very small embedded devices.

By the end of this training, participants will be able to:

[outline] =>

Introduction

Overview of TensorFlow Lite Features

Constraints of a Microcontroller

Getting Started

Creating an Audio Detection System

Serializing the Code

Working with Microcontroller'ss C++ Libraries

Verifying the Results

Creating an Image Detection System

Deploying an AI-enabled Device

Troubleshooting

Summary and Conclusion

[language] => en [duration] => 21 [status] => published [changed] => 1700037627 [source_title] => Tensorflow Lite for Microcontrollers [source_language] => en [cert_code] => [weight] => 0 [excluded_sites] => [use_mt] => stdClass Object ( [field_overview] => [field_course_outline] => [field_prerequisits] => [field_overview_in_category] => ) [cc] => tensorflowlitemicro ) [chainer] => stdClass Object ( [course_code] => chainer [hr_nid] => 372843 [title] => Deep Learning Neural Networks with Chainer [requirements] =>

Audience

[overview] =>

Chainer is an open source framework based on Python, built for accelerating research and implementing neural network models. It provides flexible, efficient, and simplified approaches to developing deep learning algorithms.

This instructor-led, live training (online or onsite) is aimed at researchers and developers who wish to use Chainer to build and train neural networks in Python while making the code easy to debug.

By the end of this training, participants will be able to:

Format of the Course

Course Customization Options

[category_overview] =>

This instructor-led, live training in <loc> (online or onsite) is aimed at researchers and developers who wish to use Chainer to build and train neural networks in Python while making the code easy to debug.

By the end of this training, participants will be able to:

[outline] =>

Introduction

Getting Started

Training Neural Networks in Chainer

Working with GPUs in Chainer

Implementing Other Neural Network Models

Troubleshooting

Summary and Conclusion

[language] => en [duration] => 14 [status] => published [changed] => 1700037772 [source_title] => Deep Learning Neural Networks with Chainer [source_language] => en [cert_code] => [weight] => 0 [excluded_sites] => [use_mt] => stdClass Object ( [field_overview] => [field_course_outline] => [field_prerequisits] => [field_overview_in_category] => ) [cc] => chainer ) [horovod] => stdClass Object ( [course_code] => horovod [hr_nid] => 372747 [title] => Distributed Deep Learning with Horovod [requirements] =>

Audience

[overview] =>

Horovod is an open source software framework, designed for processing fast and efficient distributed deep learning models using TensorFlow, Keras, PyTorch, and Apache MXNet. It can scale up a single-GPU training script to run on multiple GPUs or hosts with minimal code changes.

This instructor-led, live training (online or onsite) is aimed at developers or data scientists who wish to use Horovod to run distributed deep learning trainings and scale it up to run across multiple GPUs in parallel.

By the end of this training, participants will be able to:

Format of the Course

Course Customization Options

[category_overview] =>

This instructor-led, live training in <loc> (online or onsite) is aimed at developers or data scientists who wish to use Horovod to run distributed deep learning trainings and scale it up to run across multiple GPUs in parallel.

By the end of this training, participants will be able to:

[outline] =>

Introduction

Installing and Configuring Horovod

Running Distributed Training

Optimizing Distributed Training Processes

Troubleshooting

Summary and Conclusion

[language] => en [duration] => 7 [status] => published [changed] => 1700037772 [source_title] => Distributed Deep Learning with Horovod [source_language] => en [cert_code] => [weight] => 0 [excluded_sites] => [use_mt] => stdClass Object ( [field_overview] => [field_course_outline] => [field_prerequisits] => [field_overview_in_category] => ) [cc] => horovod ) [fpgaopenvino] => stdClass Object ( [course_code] => fpgaopenvino [hr_nid] => 339527 [title] => Accelerating Deep Learning with FPGA and OpenVINO [requirements] =>

Audience

[overview] =>

An FPGA (Field Programmable Gate Array) is an integrated circuit that can be used to accelerate deep learning computations. OpenVINO is an open source toolkit for optimizing Deep Learning models on Intel hardware.

This instructor-led, live training (online or onsite) is aimed at data scientists who wish to accelerate real-time machine learning applications and deploy them at scale.

By the end of this training, participants will be able to:

Format of the Course

Course Customization Options

[category_overview] =>

This instructor-led, live training in <loc> (online or onsite) is aimed at data scientists who wish to accelerate real-time machine learning applications and deploy them at scale.

By the end of this training, participants will be able to:

[outline] =>

Introduction

Overview the Languages, Tools, and Libraries Needed for Accelerating a Computer Vision Application

Setting up OpenVINO

Overview of OpenVINO Toolkit and its Components

Understanding Deep Learning Acceleration GPU and FPGA

Writing Software That Targets FPGA

Converting a Model Format for an Inference Engine

Mapping Network Topologies onto FPGA Architecture

Using an Acceleration Stack to Enable an FPGA Cluster

Setting up an Application to Discover an FPGA Accelerator

Deploying the Application for Real World Image Recognition

Troubleshooting

Summary and Conclusion

[language] => en [duration] => 35 [status] => published [changed] => 1700037620 [source_title] => Accelerating Deep Learning with FPGA and OpenVINO [source_language] => en [cert_code] => [weight] => 0 [excluded_sites] => [use_mt] => stdClass Object ( [field_overview] => [field_course_outline] => [field_prerequisits] => [field_overview_in_category] => ) [cc] => fpgaopenvino ) [mxnet] => stdClass Object ( [course_code] => mxnet [hr_nid] => 339519 [title] => Building Deep Learning Models with Apache MXNet [requirements] =>

Audience

[overview] =>

MXNet is a flexible, open-source Deep Learning library that is popular for research prototyping and production. Together with the high-level Gluon API interface, Apache MXNet is a powerful alternative to TensorFlow and PyTorch.

This instructor-led, live training (online or onsite) is aimed at data scientists who wish to use Apache MXNet to build and deploy a deep learning model for image recognition.

By the end of this training, participants will be able to:

Format of the Course

Course Customization Options

[category_overview] =>

This instructor-led, live training in (online or onsite) is aimed at data scientists who wish to use Apache MXNet's to build and deploy a deep learning model for image recognition.

By the end of this training, participants will be able to:

[outline] =>

Introduction

Deep Learning Principles and the Deep Learning Ecosystem

Overview of Apache MXNet Features and Architecture

Setup

Working with Data

Developing a Deep Learning Model

Deploying the Model

MXNet Security Best Practices

Troubleshooting

Summary and Conclusion

[language] => en [duration] => 21 [status] => published [changed] => 1700037620 [source_title] => Building Deep Learning Models with Apache MXNet [source_language] => en [cert_code] => [weight] => 0 [excluded_sites] => [use_mt] => stdClass Object ( [field_overview] => [field_course_outline] => [field_prerequisits] => [field_overview_in_category] => ) [cc] => mxnet ) [keras] => stdClass Object ( [course_code] => keras [hr_nid] => 314371 [title] => Deep Learning with Keras [requirements] =>

Audience

[overview] =>

Keras is a high-level neural networks API for fast development and experimentation. It runs on top of TensorFlow, CNTK, or Theano.

This instructor-led, live training (online or onsite) is aimed at technical persons who wish to apply deep learning model to image recognition applications.

By the end of this training, participants will be able to:

Format of the Course

Course Customization Options

[category_overview] =>

This instructor-led, live training in <loc> (online or onsite) is aimed at technical persons who wish to apply deep learning model to image recognition applications.

By the end of this training, participants will be able to:

[outline] =>

Introduction

Overview of Neural Networks

Understanding Convolutional Networks

Setting up Keras

Overview of Keras Features and Architecture

Overview of Keras Syntax

Understanding How a Keras Model Organize Layers

Configuring the Keras Backend (TensorFlow or Theano)

Implementing an Unsupervised Learning Model

Analyzing Images with a Convolutional Neural Network (CNN)

Preprocessing Data

Training the Model

Training on CPU vs GPU vs TPU

Evaluating the Model

Using a Pre-trained Deep Learning Model

Setting up a Recurrent Neural Network (RNN)

Debugging the Model

Saving the Model

Deploying the Model

Monitoring a Keras Model with TensorBoard

Troubleshooting

Summary and Conclusion

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Av Eloy Alfaro [vfdc] => 200.00 [prices] => Array ( [1] => Array ( [remote guaranteed] => 2312 [classroom guaranteed] => 2512 [remote guaranteed per delegate] => 2312 [delegates] => 1 [adp] => 687 [classroom guaranteed per delegate] => 2512 ) [2] => Array ( [remote guaranteed] => 3000 [classroom guaranteed] => 3250 [remote guaranteed per delegate] => 1500 [delegates] => 2 [adp] => 687 [classroom guaranteed per delegate] => 1625 ) [3] => Array ( [remote guaranteed] => 3687 [classroom guaranteed] => 3987 [remote guaranteed per delegate] => 1229 [delegates] => 3 [adp] => 687 [classroom guaranteed per delegate] => 1329 ) [4] => Array ( [remote guaranteed] => 4372 [classroom guaranteed] => 4724 [remote guaranteed per delegate] => 1093 [delegates] => 4 [adp] => 687 [classroom guaranteed per delegate] => 1181 ) [5] => Array ( [remote guaranteed] => 5060 [classroom guaranteed] => 5460 [remote guaranteed per delegate] => 1012 [delegates] => 5 [adp] => 687 [classroom guaranteed per delegate] => 1092 ) [6] => Array ( [remote guaranteed] => 5748 [classroom guaranteed] => 6198 [remote guaranteed per delegate] => 958 [delegates] => 6 [adp] => 687 [classroom guaranteed per delegate] => 1033 ) [7] => Array ( [remote guaranteed] => 6433 [classroom guaranteed] => 6937 [remote guaranteed per delegate] => 919 [delegates] => 7 [adp] => 687 [classroom guaranteed per delegate] => 991 ) [8] => Array ( [remote guaranteed] => 7120 [classroom guaranteed] => 7672 [remote guaranteed per delegate] => 890 [delegates] => 8 [adp] => 687 [classroom guaranteed per delegate] => 959 ) [9] => Array ( [remote guaranteed] => 7812 [classroom guaranteed] => 8406 [remote guaranteed per delegate] => 868 [delegates] => 9 [adp] => 687 [classroom guaranteed per delegate] => 934 ) [10] => Array ( [remote guaranteed] => 8500 [classroom guaranteed] => 9150 [remote guaranteed per delegate] => 850 [delegates] => 10 [adp] => 687 [classroom guaranteed per delegate] => 915 ) ) ) ) ) ) [remote] => Array ( [1] => Array ( [remote guaranteed] => 2312 [remote guaranteed per delegate] => 2312 [adp] => 687 ) [2] => Array ( [remote guaranteed] => 3000 [remote guaranteed per delegate] => 1500 [adp] => 687 ) [3] => Array ( [remote guaranteed] => 3687 [remote guaranteed per delegate] => 1229 [adp] => 687 ) [4] => Array ( [remote guaranteed] => 4372 [remote guaranteed per delegate] => 1093 [adp] => 687 ) [5] => Array ( [remote guaranteed] => 5060 [remote guaranteed per delegate] => 1012 [adp] => 687 ) [6] => Array ( [remote guaranteed] => 5748 [remote guaranteed per delegate] => 958 [adp] => 687 ) [7] => Array ( [remote guaranteed] => 6433 [remote guaranteed per delegate] => 919 [adp] => 687 ) [8] => Array ( [remote guaranteed] => 7120 [remote guaranteed per delegate] => 890 [adp] => 687 ) [9] => Array ( [remote guaranteed] => 7812 [remote guaranteed per delegate] => 868 [adp] => 687 ) [10] => Array ( [remote guaranteed] => 8500 [remote guaranteed per delegate] => 850 [adp] => 687 ) ) [currency] => USD ) [5] => Array ( ) [6] => Array ( ) [7] => 0 [8] => 1 [9] => [10] => ) ) [4] => Array ( [file] => /apps/nobleprog-website/core/routes.php [line] => 19 [function] => course_menu_callback [args] => Array ( [0] => /en/cc/mlbigdata ) ) [5] => Array ( [file] => /apps/nobleprog-website/__index.php [line] => 100 [args] => Array ( [0] => /apps/nobleprog-website/core/routes.php ) [function] => require_once ) [6] => Array ( [file] => /apps/nobleprog-website/_index.php [line] => 26 [args] => Array ( [0] => /apps/nobleprog-website/__index.php ) [function] => include_once ) [7] => Array ( [file] => /apps/hitra7/index.php [line] => 54 [args] => Array ( [0] => /apps/nobleprog-website/_index.php ) [function] => include_once ) ) Machine Learning and Big Data Training Course

Course Outline

Introduction

History, Evolution and Trends for Machine Learning

The Role of Big Data in Machine Learning

Infrastructure for Managing Big Data

Using Historical and Real-time Data to Predict Behavior

Case Study: Machine Learning Across Industries

Evaluating Existing Applications and Capabilities

Upskilling for Machine Learning

Tools for Implementing Machine Learning

Cloud vs On-Premise Services

Understanding the Data Middle Backend

Overview of Data Mining and Analysis

Combining Machine Learning with Data Mining

Case Study: Deploying Intelligent Applications to Deliver Personalized Experiences to Users

Summary and Conclusion

Requirements

  • An understanding of database concepts
  • Experience with software application development

Audience

  • Developers
 7 Hours

Number of participants



Price per participant

Open Training Courses require 5+ participants.

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[language] => en [duration] => 7 [status] => published [changed] => 1715350267 [source_title] => Machine Learning and Big Data [source_language] => en [cert_code] => [weight] => 0 [excluded_sites] => [use_mt] => stdClass Object ( [field_overview] => [field_course_outline] => [field_prerequisits] => [field_overview_in_category] => ) ) [1] => Array ( [0] => stdClass Object ( [tid] => 766 [alias] => big-data-training [name] => Big Data [english_name] => Big Data [consulting_option] => available_promoted ) [1] => stdClass Object ( [tid] => 943 [alias] => machine-learning-training [name] => Machine Learning [english_name] => Machine Learning [consulting_option] => ) ) [2] => mlbigdata [3] => Array ( [outlines] => Array ( [h2oautoml] => stdClass Object ( [course_code] => h2oautoml [hr_nid] => 306590 [title] => H2O AutoML [requirements] =>

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[language] => en [duration] => 14 [status] => published [changed] => 1712161736 [source_title] => H2O AutoML [source_language] => en [cert_code] => [weight] => 0 [excluded_sites] => [use_mt] => stdClass Object ( [field_overview] => [field_course_outline] => [field_prerequisits] => [field_overview_in_category] => ) [cc] => h2oautoml ) [autosklearn] => stdClass Object ( [course_code] => autosklearn [hr_nid] => 306518 [title] => AutoML with Auto-sklearn [requirements] =>

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Course Customization Options

[category_overview] =>

This instructor-led, live training in <loc> (online or onsite) is aimed at machine learning practitioners who wish to use Auto-sklearn to automate the process of selecting and optimizing a machine learning model.

By the end of this training, participants will be able to:

[outline] =>

Introduction

Setting up a Working Environment

Installing Auto-sklearn

Anatomy of a Standard Machine Learning Workflow

How Auto-sklearn Automates the Machine Learning Workflow

Searching for the Best Neural Network Architecture with NAS (Neural Architecture Search)

Case Study: AutoML with Auto-sklearn

Downloading a Dataset

Building a Machine Learning Model

Training and Testing the Model

Tuning the Hyperparameters

Building, Training, and Testing Additional Models

Tweaking the Hyperparameters to Improve Accuracy

Configuring Auto-sklearn for Deep Learning Models

Troubleshooting

Summary and Conclusion

[language] => en [duration] => 14 [status] => published [changed] => 1712075696 [source_title] => AutoML with Auto-sklearn [source_language] => en [cert_code] => [weight] => 0 [excluded_sites] => [use_mt] => stdClass Object ( [field_overview] => [field_course_outline] => [field_prerequisits] => [field_overview_in_category] => ) [cc] => autosklearn ) [autokeras] => stdClass Object ( [course_code] => autokeras [hr_nid] => 306514 [title] => AutoML with Auto-Keras [requirements] =>

Audience

[overview] =>

Auto-Keras (Also known as Autokeras or Auto Keras) is an open source Python library for automated machine learning (AutoML).

This instructor-led, live training (online or onsite) is aimed at data scientists as well as less technical persons who wish to use Auto-Keras to automate the process of selecting and optimizing a machine learning model.

By the end of this training, participants will be able to:

Format of the Course

Course Customization Options

[category_overview] =>

This instructor-led, live training in <loc> (online or onsite) is aimed at data scientists as well as less technical persons who wish to use Auto-Keras to automate the process of selecting and optimizing a machine learning model.

By the end of this training, participants will be able to:

[outline] =>

Introduction

Setting up a Working Environment

Installing Auto-Keras

Anatomy of a Standard Machine Learning Workflow

How Auto-Keras Automates the Machine Learning Workflow

Searching for the Best Neural Network Architecture with NAS (Neural Architecture Search)

Case Study: AutoML with Auto-Keras

Downloading a Dataset

Building a Machine Learning Model

Training and Testing the Model

Tuning the Hyperparameters

Building, Training, and Testing Additional Models

Tweaking the Hyperparameters to Improve Accuracy

Configuring Auto-Keras for Deep Learning Models

Troubleshooting

Summary and Conclusion

[language] => en [duration] => 14 [status] => published [changed] => 1712161692 [source_title] => AutoML with Auto-Keras [source_language] => en [cert_code] => [weight] => 0 [excluded_sites] => [use_mt] => stdClass Object ( [field_overview] => [field_course_outline] => [field_prerequisits] => [field_overview_in_category] => ) [cc] => autokeras ) [advstablediffusion] => stdClass Object ( [course_code] => advstablediffusion [hr_nid] => 437195 [title] => Advanced Stable Diffusion: Deep Learning for Text-to-Image Generation [requirements] =>

Audience

[overview] =>

Stable Diffusion is a powerful deep learning model that can generate detailed images based on text descriptions. 

This instructor-led, live training (online or onsite) is aimed at intermediate to advanced-level data scientists, machine learning engineers, deep learning researchers, and computer vision experts who wish to expand their knowledge and skills in deep learning for text-to-image generation.

By the end of this training, participants will be able to:

Format of the Course

Course Customization Options

[category_overview] =>

This instructor-led, live training in <loc> (online or onsite) is aimed at intermediate to advanced-level data scientists, machine learning engineers, deep learning researchers, and computer vision experts who wish to expand their knowledge and skills in deep learning for text-to-image generation.

By the end of this training, participants will be able to:

[outline] =>

Introduction to Advanced Stable Diffusion

Advanced Text-to-Image Generation Techniques with Stable Diffusion

Performance Optimization and Scaling for Stable Diffusion

Hyperparameter Tuning and Generalization with Stable Diffusion

Integrating Stable Diffusion with Other Deep Learning Frameworks and Tools

Debugging and Troubleshooting Stable Diffusion Models

Summary and Next Steps

[language] => en [duration] => 21 [status] => published [changed] => 1712162957 [source_title] => Advanced Stable Diffusion: Deep Learning for Text-to-Image Generation [source_language] => en [cert_code] => [weight] => -1002 [excluded_sites] => [use_mt] => stdClass Object ( [field_overview] => [field_course_outline] => [field_prerequisits] => [field_overview_in_category] => ) [cc] => advstablediffusion ) [stablediffusion] => stdClass Object ( [course_code] => stablediffusion [hr_nid] => 437191 [title] => Introduction to Stable Diffusion for Text-to-Image Generation [requirements] =>

Audience

[overview] =>

Stable Diffusion is a powerful deep learning model that can generate detailed images based on text descriptions. 

This instructor-led, live training (online or onsite) is aimed at data scientists, machine learning engineers, and computer vision researchers who wish to leverage Stable Diffusion to generate high-quality images for a variety of use cases.

By the end of this training, participants will be able to:

Format of the Course

Course Customization Options

[category_overview] =>

This instructor-led, live training in (online or onsite) is aimed at data scientists, machine learning engineers, and computer vision researchers who wish to leverage Stable Diffusion to generate high-quality images for a variety of use cases.

By the end of this training, participants will be able to:

[outline] =>

Introduction to Stable Diffusion

Building Stable Diffusion Models

Advanced Stable Diffusion Techniques

Optimizing Stable Diffusion Models

Case Studies and Best Practices

Summary and Next Steps

[language] => en [duration] => 21 [status] => published [changed] => 1712157732 [source_title] => Introduction to Stable Diffusion for Text-to-Image Generation [source_language] => en [cert_code] => [weight] => -1001 [excluded_sites] => [use_mt] => stdClass Object ( [field_overview] => [field_course_outline] => [field_prerequisits] => [field_overview_in_category] => ) [cc] => stablediffusion ) [alphafold] => stdClass Object ( [course_code] => alphafold [hr_nid] => 396067 [title] => AlphaFold [requirements] =>

Audience

[overview] =>

AlphaFold is an Artificial Intelligence (AI) system that performs the prediction of protein structures. It is developed by Alphabet’s/Google’s DeepMind as a deep learning system that can accurately predict 3D models of protein structures.

This instructor-led, live training (online or onsite) is aimed at biologists who wish to understand how AlphaFold works and use AlphaFold models as guides in their experimental studies.

By the end of this training, participants will be able to:

Format of the Course

Course Customization Options

[category_overview] =>

This instructor-led, live training in <loc> (online or onsite) is aimed at biologists who wish to understand how AlphaFold works and use AlphaFold models as guides in their experimental studies.

By the end of this training, participants will be able to:

[outline] =>

Introduction to AlphaFold

How Does AlphaFold Work?

Accessing AlphaFold

AlphaFold Protein Structure Database

AlphaFold Colab

AlphaFold Open Source Code

Interpreting AlphaFold Predictions

AlphaFold Use Cases

Summary and Next Steps

[language] => en [duration] => 7 [status] => published [changed] => 1700037846 [source_title] => AlphaFold [source_language] => en [cert_code] => [weight] => -1001 [excluded_sites] => [use_mt] => stdClass Object ( [field_overview] => [field_course_outline] => [field_prerequisits] => [field_overview_in_category] => ) [cc] => alphafold ) [tensorflowlite] => stdClass Object ( [course_code] => tensorflowlite [hr_nid] => 341007 [title] => TensorFlow Lite for Embedded Linux [requirements] =>

Audience

[overview] =>

TensorFlow Lite is an open source deep learning framework for executing models on mobile and embedded devices with limited compute and memory resources.

This instructor-led, live training (online or onsite) is aimed at developers who wish to use TensorFlow Lite to deploy deep learning models on embedded devices.

By the end of this training, participants will be able to:

Format of the Course

Course Customization Options

[category_overview] =>

This instructor-led, live training in <loc> (online or onsite) is aimed at developers who wish to use TensorFlow Lite to deploy deep learning models on embedded devices.

By the end of this training, participants will be able to:

[outline] =>

Introduction

Overview of TensorFlow Lite Features and Operations

Setting up TensorFlow Lite

Choosing a Model to Run on a Device

Customizing a Pre-trained Model

Converting a Model

Running a Prediction Model

Accelerating Model Operations

Adding Model Operations

Optimizing the Model

Troubleshooting

Summary and Conclusion

[language] => en [duration] => 21 [status] => published [changed] => 1700037625 [source_title] => TensorFlow Lite for Embedded Linux [source_language] => en [cert_code] => [weight] => 0 [excluded_sites] => [use_mt] => stdClass Object ( [field_overview] => [field_course_outline] => [field_prerequisits] => [field_overview_in_category] => ) [cc] => tensorflowlite ) [tensorflowliteandroid] => stdClass Object ( [course_code] => tensorflowliteandroid [hr_nid] => 341059 [title] => TensorFlow Lite for Android [requirements] =>

Audience

[overview] =>

TensorFlow Lite is an open source deep learning framework for mobile devices and embedded systems.

This instructor-led, live training (online or onsite) is aimed at developers who wish to use TensorFlow Lite to develop mobile applications with deep learning capabilities.

By the end of this training, participants will be able to:

Format of the Course

Course Customization Options

[category_overview] =>

This instructor-led, live training in <loc> (online or onsite) is aimed at developers who wish to use TensorFlow Lite to develop mobile applications with deep learning capabilities.

By the end of this training, participants will be able to:

[outline] =>

Introduction

Overview of TensorFlow Lite Features and Design

Machine Learning and Deep Learning Fundamentals

Preparing the Mobile App Development Environment

Creating an App for Object Recognition

Setting up TensorFlow Lite

Selecting a TensorFlow Model

Converting the TensorFlow Model

Loading the TensorFlow Model onto a Mobile Device

Optimizing the TensorFlow Model for Mobile Devices

Adding Chat Capabilities for Smarter Replies

Loading a Pre-trained TensorFlow Model

Retraining a TensorFlow Model

Pre-processing a Dataset

Setting the Hyperparameters

Deploying the AI Enabled App

Running TensorFlow Models on Other Embedded Devices

Troubleshooting

Summary and Conclusion

[language] => en [duration] => 21 [status] => published [changed] => 1700037626 [source_title] => TensorFlow Lite for Android [source_language] => en [cert_code] => [weight] => 0 [excluded_sites] => [use_mt] => stdClass Object ( [field_overview] => [field_course_outline] => [field_prerequisits] => [field_overview_in_category] => ) [cc] => tensorflowliteandroid ) [tensorflowliteios] => stdClass Object ( [course_code] => tensorflowliteios [hr_nid] => 341271 [title] => TensorFlow Lite for iOS [requirements] =>

Audience

[overview] =>

TensorFlow Lite is an open source deep learning framework for mobile devices and embedded systems.

This instructor-led, live training (online or onsite) is aimed at developers who wish to use TensorFlow Lite to develop iOS mobile applications with deep learning capabilities.

By the end of this training, participants will be able to:

Format of the Course

Course Customization Options

[category_overview] =>

This instructor-led, live training in (online or onsite) is aimed at developers who wish to use TensorFlow Lite to develop iOS mobile applications with deep learning capabilities.

By the end of this training, participants will be able to:

[outline] =>

Introduction

Overview of TensorFlow Lite Features and Workflow

Preparing the Development Environment

Capturing an Image with a Device Camera

Creating an App for Object Detection

Creating an App for Image Classification

Customizing the Model and Data

Optimizing the TensorFlow Model

Exploring Alternative Models

Deploying the AI Enabled iOS App

Troubleshooting

Summary and Conclusion

[language] => en [duration] => 21 [status] => published [changed] => 1700037628 [source_title] => TensorFlow Lite for iOS [source_language] => en [cert_code] => [weight] => 0 [excluded_sites] => [use_mt] => stdClass Object ( [field_overview] => [field_course_outline] => [field_prerequisits] => [field_overview_in_category] => ) [cc] => tensorflowliteios ) [tensorflowlitemicro] => stdClass Object ( [course_code] => tensorflowlitemicro [hr_nid] => 341211 [title] => Tensorflow Lite for Microcontrollers [requirements] =>

Audience

[overview] =>

TensorFlow Lite for Microcontrollers is a port of TensorFlow Lite designed to run machine learning models on microcontrollers and other devices with limited memory.

This instructor-led, live training (online or onsite) is aimed at engineers who wish to write, load and run machine learning models on very small embedded devices.

By the end of this training, participants will be able to:

Format of the Course

Course Customization Options

[category_overview] =>

This instructor-led, live training in <loc> (online or onsite) is aimed at engineers who wish to write, load and run machine learning models on very small embedded devices.

By the end of this training, participants will be able to:

[outline] =>

Introduction

Overview of TensorFlow Lite Features

Constraints of a Microcontroller

Getting Started

Creating an Audio Detection System

Serializing the Code

Working with Microcontroller'ss C++ Libraries

Verifying the Results

Creating an Image Detection System

Deploying an AI-enabled Device

Troubleshooting

Summary and Conclusion

[language] => en [duration] => 21 [status] => published [changed] => 1700037627 [source_title] => Tensorflow Lite for Microcontrollers [source_language] => en [cert_code] => [weight] => 0 [excluded_sites] => [use_mt] => stdClass Object ( [field_overview] => [field_course_outline] => [field_prerequisits] => [field_overview_in_category] => ) [cc] => tensorflowlitemicro ) [chainer] => stdClass Object ( [course_code] => chainer [hr_nid] => 372843 [title] => Deep Learning Neural Networks with Chainer [requirements] =>

Audience

[overview] =>

Chainer is an open source framework based on Python, built for accelerating research and implementing neural network models. It provides flexible, efficient, and simplified approaches to developing deep learning algorithms.

This instructor-led, live training (online or onsite) is aimed at researchers and developers who wish to use Chainer to build and train neural networks in Python while making the code easy to debug.

By the end of this training, participants will be able to:

Format of the Course

Course Customization Options

[category_overview] =>

This instructor-led, live training in <loc> (online or onsite) is aimed at researchers and developers who wish to use Chainer to build and train neural networks in Python while making the code easy to debug.

By the end of this training, participants will be able to:

[outline] =>

Introduction

Getting Started

Training Neural Networks in Chainer

Working with GPUs in Chainer

Implementing Other Neural Network Models

Troubleshooting

Summary and Conclusion

[language] => en [duration] => 14 [status] => published [changed] => 1700037772 [source_title] => Deep Learning Neural Networks with Chainer [source_language] => en [cert_code] => [weight] => 0 [excluded_sites] => [use_mt] => stdClass Object ( [field_overview] => [field_course_outline] => [field_prerequisits] => [field_overview_in_category] => ) [cc] => chainer ) [horovod] => stdClass Object ( [course_code] => horovod [hr_nid] => 372747 [title] => Distributed Deep Learning with Horovod [requirements] =>

Audience

[overview] =>

Horovod is an open source software framework, designed for processing fast and efficient distributed deep learning models using TensorFlow, Keras, PyTorch, and Apache MXNet. It can scale up a single-GPU training script to run on multiple GPUs or hosts with minimal code changes.

This instructor-led, live training (online or onsite) is aimed at developers or data scientists who wish to use Horovod to run distributed deep learning trainings and scale it up to run across multiple GPUs in parallel.

By the end of this training, participants will be able to:

Format of the Course

Course Customization Options

[category_overview] =>

This instructor-led, live training in <loc> (online or onsite) is aimed at developers or data scientists who wish to use Horovod to run distributed deep learning trainings and scale it up to run across multiple GPUs in parallel.

By the end of this training, participants will be able to:

[outline] =>

Introduction

Installing and Configuring Horovod

Running Distributed Training

Optimizing Distributed Training Processes

Troubleshooting

Summary and Conclusion

[language] => en [duration] => 7 [status] => published [changed] => 1700037772 [source_title] => Distributed Deep Learning with Horovod [source_language] => en [cert_code] => [weight] => 0 [excluded_sites] => [use_mt] => stdClass Object ( [field_overview] => [field_course_outline] => [field_prerequisits] => [field_overview_in_category] => ) [cc] => horovod ) [fpgaopenvino] => stdClass Object ( [course_code] => fpgaopenvino [hr_nid] => 339527 [title] => Accelerating Deep Learning with FPGA and OpenVINO [requirements] =>

Audience

[overview] =>

An FPGA (Field Programmable Gate Array) is an integrated circuit that can be used to accelerate deep learning computations. OpenVINO is an open source toolkit for optimizing Deep Learning models on Intel hardware.

This instructor-led, live training (online or onsite) is aimed at data scientists who wish to accelerate real-time machine learning applications and deploy them at scale.

By the end of this training, participants will be able to:

Format of the Course

Course Customization Options

[category_overview] =>

This instructor-led, live training in <loc> (online or onsite) is aimed at data scientists who wish to accelerate real-time machine learning applications and deploy them at scale.

By the end of this training, participants will be able to:

[outline] =>

Introduction

Overview the Languages, Tools, and Libraries Needed for Accelerating a Computer Vision Application

Setting up OpenVINO

Overview of OpenVINO Toolkit and its Components

Understanding Deep Learning Acceleration GPU and FPGA

Writing Software That Targets FPGA

Converting a Model Format for an Inference Engine

Mapping Network Topologies onto FPGA Architecture

Using an Acceleration Stack to Enable an FPGA Cluster

Setting up an Application to Discover an FPGA Accelerator

Deploying the Application for Real World Image Recognition

Troubleshooting

Summary and Conclusion

[language] => en [duration] => 35 [status] => published [changed] => 1700037620 [source_title] => Accelerating Deep Learning with FPGA and OpenVINO [source_language] => en [cert_code] => [weight] => 0 [excluded_sites] => [use_mt] => stdClass Object ( [field_overview] => [field_course_outline] => [field_prerequisits] => [field_overview_in_category] => ) [cc] => fpgaopenvino ) [mxnet] => stdClass Object ( [course_code] => mxnet [hr_nid] => 339519 [title] => Building Deep Learning Models with Apache MXNet [requirements] =>

Audience

[overview] =>

MXNet is a flexible, open-source Deep Learning library that is popular for research prototyping and production. Together with the high-level Gluon API interface, Apache MXNet is a powerful alternative to TensorFlow and PyTorch.

This instructor-led, live training (online or onsite) is aimed at data scientists who wish to use Apache MXNet to build and deploy a deep learning model for image recognition.

By the end of this training, participants will be able to:

Format of the Course

Course Customization Options

[category_overview] =>

This instructor-led, live training in (online or onsite) is aimed at data scientists who wish to use Apache MXNet's to build and deploy a deep learning model for image recognition.

By the end of this training, participants will be able to:

[outline] =>

Introduction

Deep Learning Principles and the Deep Learning Ecosystem

Overview of Apache MXNet Features and Architecture

Setup

Working with Data

Developing a Deep Learning Model

Deploying the Model

MXNet Security Best Practices

Troubleshooting

Summary and Conclusion

[language] => en [duration] => 21 [status] => published [changed] => 1700037620 [source_title] => Building Deep Learning Models with Apache MXNet [source_language] => en [cert_code] => [weight] => 0 [excluded_sites] => [use_mt] => stdClass Object ( [field_overview] => [field_course_outline] => [field_prerequisits] => [field_overview_in_category] => ) [cc] => mxnet ) [keras] => stdClass Object ( [course_code] => keras [hr_nid] => 314371 [title] => Deep Learning with Keras [requirements] =>

Audience

[overview] =>

Keras is a high-level neural networks API for fast development and experimentation. It runs on top of TensorFlow, CNTK, or Theano.

This instructor-led, live training (online or onsite) is aimed at technical persons who wish to apply deep learning model to image recognition applications.

By the end of this training, participants will be able to:

Format of the Course

Course Customization Options

[category_overview] =>

This instructor-led, live training in <loc> (online or onsite) is aimed at technical persons who wish to apply deep learning model to image recognition applications.

By the end of this training, participants will be able to:

[outline] =>

Introduction

Overview of Neural Networks

Understanding Convolutional Networks

Setting up Keras

Overview of Keras Features and Architecture

Overview of Keras Syntax

Understanding How a Keras Model Organize Layers

Configuring the Keras Backend (TensorFlow or Theano)

Implementing an Unsupervised Learning Model

Analyzing Images with a Convolutional Neural Network (CNN)

Preprocessing Data

Training the Model

Training on CPU vs GPU vs TPU

Evaluating the Model

Using a Pre-trained Deep Learning Model

Setting up a Recurrent Neural Network (RNN)

Debugging the Model

Saving the Model

Deploying the Model

Monitoring a Keras Model with TensorBoard

Troubleshooting

Summary and Conclusion

[language] => en [duration] => 21 [status] => published [changed] => 1700037517 [source_title] => Deep Learning with Keras [source_language] => en [cert_code] => [weight] => 0 [excluded_sites] => [use_mt] => stdClass Object ( [field_overview] => [field_course_outline] => [field_prerequisits] => [field_overview_in_category] => ) [cc] => keras ) ) [codes] => Array ( [0] => h2oautoml [1] => autosklearn [2] => autokeras [3] => advstablediffusion [4] => stablediffusion [5] => alphafold [6] => tensorflowlite [7] => tensorflowliteandroid [8] => tensorflowliteios [9] => tensorflowlitemicro [10] => chainer [11] => horovod [12] => fpgaopenvino [13] => mxnet [14] => keras ) ) [4] => Array ( [regions] => Array ( [ec_4966] => Array ( [tid] => ec_4966 [title] => Guayaquil [sales_area] => ec_ecuador [venues] => Array ( [ec_15661446] => Array ( [vid] => ec_15661446 [title] => Guayaquil - Mall del Sol [vfdc] => 175.00 [prices] => Array ( [1] => Array ( [remote guaranteed] => 2312 [classroom guaranteed] => 2487 [remote guaranteed per delegate] => 2312 [delegates] => 1 [adp] => 687 [classroom guaranteed per delegate] => 2487 ) [2] => Array ( [remote guaranteed] => 3000 [classroom guaranteed] => 3234 [remote guaranteed per delegate] => 1500 [delegates] => 2 [adp] => 687 [classroom guaranteed per delegate] => 1617 ) [3] => Array ( [remote guaranteed] => 3687 [classroom guaranteed] => 3981 [remote guaranteed per delegate] => 1229 [delegates] => 3 [adp] => 687 [classroom guaranteed per delegate] => 1327 ) [4] => Array ( [remote guaranteed] => 4372 [classroom guaranteed] => 4728 [remote guaranteed per delegate] => 1093 [delegates] => 4 [adp] => 687 [classroom guaranteed per delegate] => 1182 ) [5] => Array ( [remote guaranteed] => 5060 [classroom guaranteed] => 5475 [remote guaranteed per delegate] => 1012 [delegates] => 5 [adp] => 687 [classroom guaranteed per delegate] => 1095 ) [6] => Array ( [remote guaranteed] => 5748 [classroom guaranteed] => 6222 [remote guaranteed per delegate] => 958 [delegates] => 6 [adp] => 687 [classroom guaranteed per delegate] => 1037 ) [7] => Array ( [remote guaranteed] => 6433 [classroom guaranteed] => 6972 [remote guaranteed per delegate] => 919 [delegates] => 7 [adp] => 687 [classroom guaranteed per delegate] => 996 ) [8] => Array ( [remote guaranteed] => 7120 [classroom guaranteed] => 7720 [remote guaranteed per delegate] => 890 [delegates] => 8 [adp] => 687 [classroom guaranteed per delegate] => 965 ) [9] => Array ( [remote guaranteed] => 7812 [classroom guaranteed] => 8460 [remote guaranteed per delegate] => 868 [delegates] => 9 [adp] => 687 [classroom guaranteed per delegate] => 940 ) [10] => Array ( [remote guaranteed] => 8500 [classroom guaranteed] => 9210 [remote guaranteed per delegate] => 850 [delegates] => 10 [adp] => 687 [classroom guaranteed per delegate] => 921 ) ) ) ) ) [ec_4967] => Array ( [tid] => ec_4967 [title] => Quito [sales_area] => ec_ecuador [venues] => Array ( [ec_15661447] => Array ( [vid] => ec_15661447 [title] => Quito - Av Eloy Alfaro [vfdc] => 200.00 [prices] => Array ( [1] => Array ( [remote guaranteed] => 2312 [classroom guaranteed] => 2512 [remote guaranteed per delegate] => 2312 [delegates] => 1 [adp] => 687 [classroom guaranteed per delegate] => 2512 ) [2] => Array ( [remote guaranteed] => 3000 [classroom guaranteed] => 3250 [remote guaranteed per delegate] => 1500 [delegates] => 2 [adp] => 687 [classroom guaranteed per delegate] => 1625 ) [3] => Array ( [remote guaranteed] => 3687 [classroom guaranteed] => 3987 [remote guaranteed per delegate] => 1229 [delegates] => 3 [adp] => 687 [classroom guaranteed per delegate] => 1329 ) [4] => Array ( [remote guaranteed] => 4372 [classroom guaranteed] => 4724 [remote guaranteed per delegate] => 1093 [delegates] => 4 [adp] => 687 [classroom guaranteed per delegate] => 1181 ) [5] => Array ( [remote guaranteed] => 5060 [classroom guaranteed] => 5460 [remote guaranteed per delegate] => 1012 [delegates] => 5 [adp] => 687 [classroom guaranteed per delegate] => 1092 ) [6] => Array ( [remote guaranteed] => 5748 [classroom guaranteed] => 6198 [remote guaranteed per delegate] => 958 [delegates] => 6 [adp] => 687 [classroom guaranteed per delegate] => 1033 ) [7] => Array ( [remote guaranteed] => 6433 [classroom guaranteed] => 6937 [remote guaranteed per delegate] => 919 [delegates] => 7 [adp] => 687 [classroom guaranteed per delegate] => 991 ) [8] => Array ( [remote guaranteed] => 7120 [classroom guaranteed] => 7672 [remote guaranteed per delegate] => 890 [delegates] => 8 [adp] => 687 [classroom guaranteed per delegate] => 959 ) [9] => Array ( [remote guaranteed] => 7812 [classroom guaranteed] => 8406 [remote guaranteed per delegate] => 868 [delegates] => 9 [adp] => 687 [classroom guaranteed per delegate] => 934 ) [10] => Array ( [remote guaranteed] => 8500 [classroom guaranteed] => 9150 [remote guaranteed per delegate] => 850 [delegates] => 10 [adp] => 687 [classroom guaranteed per delegate] => 915 ) ) ) ) ) ) [remote] => Array ( [1] => Array ( [remote guaranteed] => 2312 [remote guaranteed per delegate] => 2312 [adp] => 687 ) [2] => Array ( [remote guaranteed] => 3000 [remote guaranteed per delegate] => 1500 [adp] => 687 ) [3] => Array ( [remote guaranteed] => 3687 [remote guaranteed per delegate] => 1229 [adp] => 687 ) [4] => Array ( [remote guaranteed] => 4372 [remote guaranteed per delegate] => 1093 [adp] => 687 ) [5] => Array ( [remote guaranteed] => 5060 [remote guaranteed per delegate] => 1012 [adp] => 687 ) [6] => Array ( [remote guaranteed] => 5748 [remote guaranteed per delegate] => 958 [adp] => 687 ) [7] => Array ( [remote guaranteed] => 6433 [remote guaranteed per delegate] => 919 [adp] => 687 ) [8] => Array ( [remote guaranteed] => 7120 [remote guaranteed per delegate] => 890 [adp] => 687 ) [9] => Array ( [remote guaranteed] => 7812 [remote guaranteed per delegate] => 868 [adp] => 687 ) [10] => Array ( [remote guaranteed] => 8500 [remote guaranteed per delegate] => 850 [adp] => 687 ) ) [currency] => USD ) [5] => Array ( ) [6] => Array ( ) [7] => 0 [8] => 1 [9] => [10] => ) ) [7] => Array ( [file] => /apps/nobleprog-website/core/routes.php [line] => 19 [function] => course_menu_callback [args] => Array ( [0] => /en/cc/mlbigdata ) ) [8] => Array ( [file] => /apps/nobleprog-website/__index.php [line] => 100 [args] => Array ( [0] => /apps/nobleprog-website/core/routes.php ) [function] => require_once ) [9] => Array ( [file] => /apps/nobleprog-website/_index.php [line] => 26 [args] => Array ( [0] => /apps/nobleprog-website/__index.php ) [function] => include_once ) [10] => Array ( [file] => /apps/hitra7/index.php [line] => 54 [args] => Array ( [0] => /apps/nobleprog-website/_index.php ) [function] => include_once ) ) NP URI: www.nobleprog.com.ec/en/cc/mlbigdata Undefined array key "devel_domain" /apps/nobleprog-website/includes/functions/new-modules-general-functions.php:82 Array ( [0] => Array ( [file] => /apps/nobleprog-website/includes/functions/new-modules-general-functions.php [line] => 82 [function] => myErrorHandler [args] => Array ( [0] => 2 [1] => Undefined array key "devel_domain" [2] => /apps/nobleprog-website/includes/functions/new-modules-general-functions.php [3] => 82 ) ) [1] => Array ( [file] => /apps/hitra7/drupal7/sites/all/modules/_custom/frontend/islc7/islc7.module [line] => 99 [function] => variable_get [args] => Array ( [0] => devel_domain [1] => ) ) [2] => Array ( [file] => /apps/hitra7/drupal7/sites/all/modules/_custom/frontend/islc7/islc7_block.inc [line] => 34 [function] => islc_get_site_list [args] => Array ( ) ) [3] => Array ( [file] => /apps/nobleprog-website/nptemplates/default.php [line] => 265 [function] => islc7_sites_links_array_v3 [args] => Array ( ) ) [4] => Array ( [file] => /apps/nobleprog-website/modules/course/course.php [line] => 85 [args] => Array ( [0] => /apps/nobleprog-website/nptemplates/default.php ) [function] => require_once ) [5] => Array ( [file] => /apps/nobleprog-website/modules/course/course.php [line] => 31 [function] => course_render [args] => Array ( [0] => Array ( [course_code] => mlbigdata [hr_nid] => 318287 [title] => Machine Learning and Big Data [requirements] =>

Audience

[overview] =>

This instructor-led, live training (online or onsite) is aimed at technical persons who wish to learn how to implement a machine learning strategy while maximizing the use of big data.

By the end of this training, participants will:

Format of the Course

Course Customization Options

[category_overview] =>

This instructor-led, live training in <loc> (online or onsite) is aimed at technical persons who wish to learn how to implement a machine learning strategy while maximizing the use of big data.

By the end of this training, participants will:

[outline] =>

Introduction

History, Evolution and Trends for Machine Learning

The Role of Big Data in Machine Learning

Infrastructure for Managing Big Data

Using Historical and Real-time Data to Predict Behavior

Case Study: Machine Learning Across Industries

Evaluating Existing Applications and Capabilities

Upskilling for Machine Learning

Tools for Implementing Machine Learning

Cloud vs On-Premise Services

Understanding the Data Middle Backend

Overview of Data Mining and Analysis

Combining Machine Learning with Data Mining

Case Study: Deploying Intelligent Applications to Deliver Personalized Experiences to Users

Summary and Conclusion

[language] => en [duration] => 7 [status] => published [changed] => 1715350267 [source_title] => Machine Learning and Big Data [source_language] => en [cert_code] => [weight] => 0 [excluded_sites] => [use_mt] => stdClass Object ( [field_overview] => [field_course_outline] => [field_prerequisits] => [field_overview_in_category] => ) ) [1] => Array ( [0] => stdClass Object ( [tid] => 766 [alias] => big-data-training [name] => Big Data [english_name] => Big Data [consulting_option] => available_promoted ) [1] => stdClass Object ( [tid] => 943 [alias] => machine-learning-training [name] => Machine Learning [english_name] => Machine Learning [consulting_option] => ) ) [2] => mlbigdata [3] => Array ( [outlines] => Array ( [h2oautoml] => stdClass Object ( [course_code] => h2oautoml [hr_nid] => 306590 [title] => H2O AutoML [requirements] =>

Audience

[overview] =>

H2O AutoML is an artificial intelligence platform that automates the process of building, selecting and optimizing large numbers of machine learning models.

This instructor-led, live training (online or onsite) is aimed at data scientists who wish to use H2O AutoML to automate the process of building and selecting the best machine learning algorithm and parameters.

By the end of this training, participants will be able to:

Format of the Course

Course Customization Options

[category_overview] =>

This instructor-led, live training in <loc> (online or onsite) is aimed at data scientists who wish to use H2O AutoML to automoate the process of building and selecting the best machine learning algorithm and parameters.

By the end of this training, participants will be able to:

[outline] =>

Introduction

Setting up a Working Environment

Installing H2O

Anatomy of a Standard Machine Learning Workflow

Statistical and Machine Learning Algorithms

How H2O Automates the Machine Learning Workflow

Case Study: Predicting Product Availability

Downloading a Dataset

Building a Machine Learning Model

Specify a Training Frame

Training and Cross-Validating Different Models

Tuning the Hyperparameters

Training two Stacked Ensemble Models

Generating a Leaderboard of the Best Models

Inspecting the Ensemble Composition

Training many Deep Neural Network Models

Troubleshooting

Summary and Conclusion

[language] => en [duration] => 14 [status] => published [changed] => 1712161736 [source_title] => H2O AutoML [source_language] => en [cert_code] => [weight] => 0 [excluded_sites] => [use_mt] => stdClass Object ( [field_overview] => [field_course_outline] => [field_prerequisits] => [field_overview_in_category] => ) [cc] => h2oautoml ) [autosklearn] => stdClass Object ( [course_code] => autosklearn [hr_nid] => 306518 [title] => AutoML with Auto-sklearn [requirements] =>

Audience

[overview] =>

Auto-sklearn is a Python package built around the scikit-learn machine learning library. It automatically searches for the right learning algorithm for a new machine learning dataset and optimizes its parameters.

This instructor-led, live training (online or onsite) is aimed at machine learning practitioners who wish to use Auto-sklearn to automate the process of selecting and optimizing a machine learning model.

By the end of this training, participants will be able to:

Format of the Course

Course Customization Options

[category_overview] =>

This instructor-led, live training in <loc> (online or onsite) is aimed at machine learning practitioners who wish to use Auto-sklearn to automate the process of selecting and optimizing a machine learning model.

By the end of this training, participants will be able to:

[outline] =>

Introduction

Setting up a Working Environment

Installing Auto-sklearn

Anatomy of a Standard Machine Learning Workflow

How Auto-sklearn Automates the Machine Learning Workflow

Searching for the Best Neural Network Architecture with NAS (Neural Architecture Search)

Case Study: AutoML with Auto-sklearn

Downloading a Dataset

Building a Machine Learning Model

Training and Testing the Model

Tuning the Hyperparameters

Building, Training, and Testing Additional Models

Tweaking the Hyperparameters to Improve Accuracy

Configuring Auto-sklearn for Deep Learning Models

Troubleshooting

Summary and Conclusion

[language] => en [duration] => 14 [status] => published [changed] => 1712075696 [source_title] => AutoML with Auto-sklearn [source_language] => en [cert_code] => [weight] => 0 [excluded_sites] => [use_mt] => stdClass Object ( [field_overview] => [field_course_outline] => [field_prerequisits] => [field_overview_in_category] => ) [cc] => autosklearn ) [autokeras] => stdClass Object ( [course_code] => autokeras [hr_nid] => 306514 [title] => AutoML with Auto-Keras [requirements] =>

Audience

[overview] =>

Auto-Keras (Also known as Autokeras or Auto Keras) is an open source Python library for automated machine learning (AutoML).

This instructor-led, live training (online or onsite) is aimed at data scientists as well as less technical persons who wish to use Auto-Keras to automate the process of selecting and optimizing a machine learning model.

By the end of this training, participants will be able to:

Format of the Course

Course Customization Options

[category_overview] =>

This instructor-led, live training in <loc> (online or onsite) is aimed at data scientists as well as less technical persons who wish to use Auto-Keras to automate the process of selecting and optimizing a machine learning model.

By the end of this training, participants will be able to:

[outline] =>

Introduction

Setting up a Working Environment

Installing Auto-Keras

Anatomy of a Standard Machine Learning Workflow

How Auto-Keras Automates the Machine Learning Workflow

Searching for the Best Neural Network Architecture with NAS (Neural Architecture Search)

Case Study: AutoML with Auto-Keras

Downloading a Dataset

Building a Machine Learning Model

Training and Testing the Model

Tuning the Hyperparameters

Building, Training, and Testing Additional Models

Tweaking the Hyperparameters to Improve Accuracy

Configuring Auto-Keras for Deep Learning Models

Troubleshooting

Summary and Conclusion

[language] => en [duration] => 14 [status] => published [changed] => 1712161692 [source_title] => AutoML with Auto-Keras [source_language] => en [cert_code] => [weight] => 0 [excluded_sites] => [use_mt] => stdClass Object ( [field_overview] => [field_course_outline] => [field_prerequisits] => [field_overview_in_category] => ) [cc] => autokeras ) [advstablediffusion] => stdClass Object ( [course_code] => advstablediffusion [hr_nid] => 437195 [title] => Advanced Stable Diffusion: Deep Learning for Text-to-Image Generation [requirements] =>

Audience

[overview] =>

Stable Diffusion is a powerful deep learning model that can generate detailed images based on text descriptions. 

This instructor-led, live training (online or onsite) is aimed at intermediate to advanced-level data scientists, machine learning engineers, deep learning researchers, and computer vision experts who wish to expand their knowledge and skills in deep learning for text-to-image generation.

By the end of this training, participants will be able to:

Format of the Course

Course Customization Options

[category_overview] =>

This instructor-led, live training in <loc> (online or onsite) is aimed at intermediate to advanced-level data scientists, machine learning engineers, deep learning researchers, and computer vision experts who wish to expand their knowledge and skills in deep learning for text-to-image generation.

By the end of this training, participants will be able to:

[outline] =>

Introduction to Advanced Stable Diffusion

Advanced Text-to-Image Generation Techniques with Stable Diffusion

Performance Optimization and Scaling for Stable Diffusion

Hyperparameter Tuning and Generalization with Stable Diffusion

Integrating Stable Diffusion with Other Deep Learning Frameworks and Tools

Debugging and Troubleshooting Stable Diffusion Models

Summary and Next Steps

[language] => en [duration] => 21 [status] => published [changed] => 1712162957 [source_title] => Advanced Stable Diffusion: Deep Learning for Text-to-Image Generation [source_language] => en [cert_code] => [weight] => -1002 [excluded_sites] => [use_mt] => stdClass Object ( [field_overview] => [field_course_outline] => [field_prerequisits] => [field_overview_in_category] => ) [cc] => advstablediffusion ) [stablediffusion] => stdClass Object ( [course_code] => stablediffusion [hr_nid] => 437191 [title] => Introduction to Stable Diffusion for Text-to-Image Generation [requirements] =>

Audience

[overview] =>

Stable Diffusion is a powerful deep learning model that can generate detailed images based on text descriptions. 

This instructor-led, live training (online or onsite) is aimed at data scientists, machine learning engineers, and computer vision researchers who wish to leverage Stable Diffusion to generate high-quality images for a variety of use cases.

By the end of this training, participants will be able to:

Format of the Course

Course Customization Options

[category_overview] =>

This instructor-led, live training in (online or onsite) is aimed at data scientists, machine learning engineers, and computer vision researchers who wish to leverage Stable Diffusion to generate high-quality images for a variety of use cases.

By the end of this training, participants will be able to:

[outline] =>

Introduction to Stable Diffusion

Building Stable Diffusion Models

Advanced Stable Diffusion Techniques

Optimizing Stable Diffusion Models

Case Studies and Best Practices

Summary and Next Steps

[language] => en [duration] => 21 [status] => published [changed] => 1712157732 [source_title] => Introduction to Stable Diffusion for Text-to-Image Generation [source_language] => en [cert_code] => [weight] => -1001 [excluded_sites] => [use_mt] => stdClass Object ( [field_overview] => [field_course_outline] => [field_prerequisits] => [field_overview_in_category] => ) [cc] => stablediffusion ) [alphafold] => stdClass Object ( [course_code] => alphafold [hr_nid] => 396067 [title] => AlphaFold [requirements] =>

Audience

[overview] =>

AlphaFold is an Artificial Intelligence (AI) system that performs the prediction of protein structures. It is developed by Alphabet’s/Google’s DeepMind as a deep learning system that can accurately predict 3D models of protein structures.

This instructor-led, live training (online or onsite) is aimed at biologists who wish to understand how AlphaFold works and use AlphaFold models as guides in their experimental studies.

By the end of this training, participants will be able to:

Format of the Course

Course Customization Options

[category_overview] =>

This instructor-led, live training in <loc> (online or onsite) is aimed at biologists who wish to understand how AlphaFold works and use AlphaFold models as guides in their experimental studies.

By the end of this training, participants will be able to:

[outline] =>

Introduction to AlphaFold

How Does AlphaFold Work?

Accessing AlphaFold

AlphaFold Protein Structure Database

AlphaFold Colab

AlphaFold Open Source Code

Interpreting AlphaFold Predictions

AlphaFold Use Cases

Summary and Next Steps

[language] => en [duration] => 7 [status] => published [changed] => 1700037846 [source_title] => AlphaFold [source_language] => en [cert_code] => [weight] => -1001 [excluded_sites] => [use_mt] => stdClass Object ( [field_overview] => [field_course_outline] => [field_prerequisits] => [field_overview_in_category] => ) [cc] => alphafold ) [tensorflowlite] => stdClass Object ( [course_code] => tensorflowlite [hr_nid] => 341007 [title] => TensorFlow Lite for Embedded Linux [requirements] =>

Audience

[overview] =>

TensorFlow Lite is an open source deep learning framework for executing models on mobile and embedded devices with limited compute and memory resources.

This instructor-led, live training (online or onsite) is aimed at developers who wish to use TensorFlow Lite to deploy deep learning models on embedded devices.

By the end of this training, participants will be able to:

Format of the Course

Course Customization Options

[category_overview] =>

This instructor-led, live training in <loc> (online or onsite) is aimed at developers who wish to use TensorFlow Lite to deploy deep learning models on embedded devices.

By the end of this training, participants will be able to:

[outline] =>

Introduction

Overview of TensorFlow Lite Features and Operations

Setting up TensorFlow Lite

Choosing a Model to Run on a Device

Customizing a Pre-trained Model

Converting a Model

Running a Prediction Model

Accelerating Model Operations

Adding Model Operations

Optimizing the Model

Troubleshooting

Summary and Conclusion

[language] => en [duration] => 21 [status] => published [changed] => 1700037625 [source_title] => TensorFlow Lite for Embedded Linux [source_language] => en [cert_code] => [weight] => 0 [excluded_sites] => [use_mt] => stdClass Object ( [field_overview] => [field_course_outline] => [field_prerequisits] => [field_overview_in_category] => ) [cc] => tensorflowlite ) [tensorflowliteandroid] => stdClass Object ( [course_code] => tensorflowliteandroid [hr_nid] => 341059 [title] => TensorFlow Lite for Android [requirements] =>

Audience

[overview] =>

TensorFlow Lite is an open source deep learning framework for mobile devices and embedded systems.

This instructor-led, live training (online or onsite) is aimed at developers who wish to use TensorFlow Lite to develop mobile applications with deep learning capabilities.

By the end of this training, participants will be able to:

Format of the Course

Course Customization Options

[category_overview] =>

This instructor-led, live training in <loc> (online or onsite) is aimed at developers who wish to use TensorFlow Lite to develop mobile applications with deep learning capabilities.

By the end of this training, participants will be able to:

[outline] =>

Introduction

Overview of TensorFlow Lite Features and Design

Machine Learning and Deep Learning Fundamentals

Preparing the Mobile App Development Environment

Creating an App for Object Recognition

Setting up TensorFlow Lite

Selecting a TensorFlow Model

Converting the TensorFlow Model

Loading the TensorFlow Model onto a Mobile Device

Optimizing the TensorFlow Model for Mobile Devices

Adding Chat Capabilities for Smarter Replies

Loading a Pre-trained TensorFlow Model

Retraining a TensorFlow Model

Pre-processing a Dataset

Setting the Hyperparameters

Deploying the AI Enabled App

Running TensorFlow Models on Other Embedded Devices

Troubleshooting

Summary and Conclusion

[language] => en [duration] => 21 [status] => published [changed] => 1700037626 [source_title] => TensorFlow Lite for Android [source_language] => en [cert_code] => [weight] => 0 [excluded_sites] => [use_mt] => stdClass Object ( [field_overview] => [field_course_outline] => [field_prerequisits] => [field_overview_in_category] => ) [cc] => tensorflowliteandroid ) [tensorflowliteios] => stdClass Object ( [course_code] => tensorflowliteios [hr_nid] => 341271 [title] => TensorFlow Lite for iOS [requirements] =>

Audience

[overview] =>

TensorFlow Lite is an open source deep learning framework for mobile devices and embedded systems.

This instructor-led, live training (online or onsite) is aimed at developers who wish to use TensorFlow Lite to develop iOS mobile applications with deep learning capabilities.

By the end of this training, participants will be able to:

Format of the Course

Course Customization Options

[category_overview] =>

This instructor-led, live training in (online or onsite) is aimed at developers who wish to use TensorFlow Lite to develop iOS mobile applications with deep learning capabilities.

By the end of this training, participants will be able to:

[outline] =>

Introduction

Overview of TensorFlow Lite Features and Workflow

Preparing the Development Environment

Capturing an Image with a Device Camera

Creating an App for Object Detection

Creating an App for Image Classification

Customizing the Model and Data

Optimizing the TensorFlow Model

Exploring Alternative Models

Deploying the AI Enabled iOS App

Troubleshooting

Summary and Conclusion

[language] => en [duration] => 21 [status] => published [changed] => 1700037628 [source_title] => TensorFlow Lite for iOS [source_language] => en [cert_code] => [weight] => 0 [excluded_sites] => [use_mt] => stdClass Object ( [field_overview] => [field_course_outline] => [field_prerequisits] => [field_overview_in_category] => ) [cc] => tensorflowliteios ) [tensorflowlitemicro] => stdClass Object ( [course_code] => tensorflowlitemicro [hr_nid] => 341211 [title] => Tensorflow Lite for Microcontrollers [requirements] =>

Audience

[overview] =>

TensorFlow Lite for Microcontrollers is a port of TensorFlow Lite designed to run machine learning models on microcontrollers and other devices with limited memory.

This instructor-led, live training (online or onsite) is aimed at engineers who wish to write, load and run machine learning models on very small embedded devices.

By the end of this training, participants will be able to:

Format of the Course

Course Customization Options

[category_overview] =>

This instructor-led, live training in <loc> (online or onsite) is aimed at engineers who wish to write, load and run machine learning models on very small embedded devices.

By the end of this training, participants will be able to:

[outline] =>

Introduction

Overview of TensorFlow Lite Features

Constraints of a Microcontroller

Getting Started

Creating an Audio Detection System

Serializing the Code

Working with Microcontroller'ss C++ Libraries

Verifying the Results

Creating an Image Detection System

Deploying an AI-enabled Device

Troubleshooting

Summary and Conclusion

[language] => en [duration] => 21 [status] => published [changed] => 1700037627 [source_title] => Tensorflow Lite for Microcontrollers [source_language] => en [cert_code] => [weight] => 0 [excluded_sites] => [use_mt] => stdClass Object ( [field_overview] => [field_course_outline] => [field_prerequisits] => [field_overview_in_category] => ) [cc] => tensorflowlitemicro ) [chainer] => stdClass Object ( [course_code] => chainer [hr_nid] => 372843 [title] => Deep Learning Neural Networks with Chainer [requirements] =>

Audience

[overview] =>

Chainer is an open source framework based on Python, built for accelerating research and implementing neural network models. It provides flexible, efficient, and simplified approaches to developing deep learning algorithms.

This instructor-led, live training (online or onsite) is aimed at researchers and developers who wish to use Chainer to build and train neural networks in Python while making the code easy to debug.

By the end of this training, participants will be able to:

Format of the Course

Course Customization Options

[category_overview] =>

This instructor-led, live training in <loc> (online or onsite) is aimed at researchers and developers who wish to use Chainer to build and train neural networks in Python while making the code easy to debug.

By the end of this training, participants will be able to:

[outline] =>

Introduction

Getting Started

Training Neural Networks in Chainer

Working with GPUs in Chainer

Implementing Other Neural Network Models

Troubleshooting

Summary and Conclusion

[language] => en [duration] => 14 [status] => published [changed] => 1700037772 [source_title] => Deep Learning Neural Networks with Chainer [source_language] => en [cert_code] => [weight] => 0 [excluded_sites] => [use_mt] => stdClass Object ( [field_overview] => [field_course_outline] => [field_prerequisits] => [field_overview_in_category] => ) [cc] => chainer ) [horovod] => stdClass Object ( [course_code] => horovod [hr_nid] => 372747 [title] => Distributed Deep Learning with Horovod [requirements] =>

Audience

[overview] =>

Horovod is an open source software framework, designed for processing fast and efficient distributed deep learning models using TensorFlow, Keras, PyTorch, and Apache MXNet. It can scale up a single-GPU training script to run on multiple GPUs or hosts with minimal code changes.

This instructor-led, live training (online or onsite) is aimed at developers or data scientists who wish to use Horovod to run distributed deep learning trainings and scale it up to run across multiple GPUs in parallel.

By the end of this training, participants will be able to:

Format of the Course

Course Customization Options

[category_overview] =>

This instructor-led, live training in <loc> (online or onsite) is aimed at developers or data scientists who wish to use Horovod to run distributed deep learning trainings and scale it up to run across multiple GPUs in parallel.

By the end of this training, participants will be able to:

[outline] =>

Introduction

Installing and Configuring Horovod

Running Distributed Training

Optimizing Distributed Training Processes

Troubleshooting

Summary and Conclusion

[language] => en [duration] => 7 [status] => published [changed] => 1700037772 [source_title] => Distributed Deep Learning with Horovod [source_language] => en [cert_code] => [weight] => 0 [excluded_sites] => [use_mt] => stdClass Object ( [field_overview] => [field_course_outline] => [field_prerequisits] => [field_overview_in_category] => ) [cc] => horovod ) [fpgaopenvino] => stdClass Object ( [course_code] => fpgaopenvino [hr_nid] => 339527 [title] => Accelerating Deep Learning with FPGA and OpenVINO [requirements] =>

Audience

[overview] =>

An FPGA (Field Programmable Gate Array) is an integrated circuit that can be used to accelerate deep learning computations. OpenVINO is an open source toolkit for optimizing Deep Learning models on Intel hardware.

This instructor-led, live training (online or onsite) is aimed at data scientists who wish to accelerate real-time machine learning applications and deploy them at scale.

By the end of this training, participants will be able to:

Format of the Course

Course Customization Options

[category_overview] =>

This instructor-led, live training in <loc> (online or onsite) is aimed at data scientists who wish to accelerate real-time machine learning applications and deploy them at scale.

By the end of this training, participants will be able to:

[outline] =>

Introduction

Overview the Languages, Tools, and Libraries Needed for Accelerating a Computer Vision Application

Setting up OpenVINO

Overview of OpenVINO Toolkit and its Components

Understanding Deep Learning Acceleration GPU and FPGA

Writing Software That Targets FPGA

Converting a Model Format for an Inference Engine

Mapping Network Topologies onto FPGA Architecture

Using an Acceleration Stack to Enable an FPGA Cluster

Setting up an Application to Discover an FPGA Accelerator

Deploying the Application for Real World Image Recognition

Troubleshooting

Summary and Conclusion

[language] => en [duration] => 35 [status] => published [changed] => 1700037620 [source_title] => Accelerating Deep Learning with FPGA and OpenVINO [source_language] => en [cert_code] => [weight] => 0 [excluded_sites] => [use_mt] => stdClass Object ( [field_overview] => [field_course_outline] => [field_prerequisits] => [field_overview_in_category] => ) [cc] => fpgaopenvino ) [mxnet] => stdClass Object ( [course_code] => mxnet [hr_nid] => 339519 [title] => Building Deep Learning Models with Apache MXNet [requirements] =>

Audience

[overview] =>

MXNet is a flexible, open-source Deep Learning library that is popular for research prototyping and production. Together with the high-level Gluon API interface, Apache MXNet is a powerful alternative to TensorFlow and PyTorch.

This instructor-led, live training (online or onsite) is aimed at data scientists who wish to use Apache MXNet to build and deploy a deep learning model for image recognition.

By the end of this training, participants will be able to:

Format of the Course

Course Customization Options

[category_overview] =>

This instructor-led, live training in (online or onsite) is aimed at data scientists who wish to use Apache MXNet's to build and deploy a deep learning model for image recognition.

By the end of this training, participants will be able to:

[outline] =>

Introduction

Deep Learning Principles and the Deep Learning Ecosystem

Overview of Apache MXNet Features and Architecture

Setup

Working with Data

Developing a Deep Learning Model

Deploying the Model

MXNet Security Best Practices

Troubleshooting

Summary and Conclusion

[language] => en [duration] => 21 [status] => published [changed] => 1700037620 [source_title] => Building Deep Learning Models with Apache MXNet [source_language] => en [cert_code] => [weight] => 0 [excluded_sites] => [use_mt] => stdClass Object ( [field_overview] => [field_course_outline] => [field_prerequisits] => [field_overview_in_category] => ) [cc] => mxnet ) [keras] => stdClass Object ( [course_code] => keras [hr_nid] => 314371 [title] => Deep Learning with Keras [requirements] =>

Audience

[overview] =>

Keras is a high-level neural networks API for fast development and experimentation. It runs on top of TensorFlow, CNTK, or Theano.

This instructor-led, live training (online or onsite) is aimed at technical persons who wish to apply deep learning model to image recognition applications.

By the end of this training, participants will be able to:

Format of the Course

Course Customization Options

[category_overview] =>

This instructor-led, live training in <loc> (online or onsite) is aimed at technical persons who wish to apply deep learning model to image recognition applications.

By the end of this training, participants will be able to:

[outline] =>

Introduction

Overview of Neural Networks

Understanding Convolutional Networks

Setting up Keras

Overview of Keras Features and Architecture

Overview of Keras Syntax

Understanding How a Keras Model Organize Layers

Configuring the Keras Backend (TensorFlow or Theano)

Implementing an Unsupervised Learning Model

Analyzing Images with a Convolutional Neural Network (CNN)

Preprocessing Data

Training the Model

Training on CPU vs GPU vs TPU

Evaluating the Model

Using a Pre-trained Deep Learning Model

Setting up a Recurrent Neural Network (RNN)

Debugging the Model

Saving the Model

Deploying the Model

Monitoring a Keras Model with TensorBoard

Troubleshooting

Summary and Conclusion

[language] => en [duration] => 21 [status] => published [changed] => 1700037517 [source_title] => Deep Learning with Keras [source_language] => en [cert_code] => [weight] => 0 [excluded_sites] => [use_mt] => stdClass Object ( [field_overview] => [field_course_outline] => [field_prerequisits] => [field_overview_in_category] => ) [cc] => keras ) ) [codes] => Array ( [0] => h2oautoml [1] => autosklearn [2] => autokeras [3] => advstablediffusion [4] => stablediffusion [5] => alphafold [6] => tensorflowlite [7] => tensorflowliteandroid [8] => tensorflowliteios [9] => tensorflowlitemicro [10] => chainer [11] => horovod [12] => fpgaopenvino [13] => mxnet [14] => keras ) ) [4] => Array ( [regions] => Array ( [ec_4966] => Array ( [tid] => ec_4966 [title] => Guayaquil [sales_area] => ec_ecuador [venues] => Array ( [ec_15661446] => Array ( [vid] => ec_15661446 [title] => Guayaquil - Mall del Sol [vfdc] => 175.00 [prices] => Array ( [1] => Array ( [remote guaranteed] => 2312 [classroom guaranteed] => 2487 [remote guaranteed per delegate] => 2312 [delegates] => 1 [adp] => 687 [classroom guaranteed per delegate] => 2487 ) [2] => Array ( [remote guaranteed] => 3000 [classroom guaranteed] => 3234 [remote guaranteed per delegate] => 1500 [delegates] => 2 [adp] => 687 [classroom guaranteed per delegate] => 1617 ) [3] => Array ( [remote guaranteed] => 3687 [classroom guaranteed] => 3981 [remote guaranteed per delegate] => 1229 [delegates] => 3 [adp] => 687 [classroom guaranteed per delegate] => 1327 ) [4] => Array ( [remote guaranteed] => 4372 [classroom guaranteed] => 4728 [remote guaranteed per delegate] => 1093 [delegates] => 4 [adp] => 687 [classroom guaranteed per delegate] => 1182 ) [5] => Array ( [remote guaranteed] => 5060 [classroom guaranteed] => 5475 [remote guaranteed per delegate] => 1012 [delegates] => 5 [adp] => 687 [classroom guaranteed per delegate] => 1095 ) [6] => Array ( [remote guaranteed] => 5748 [classroom guaranteed] => 6222 [remote guaranteed per delegate] => 958 [delegates] => 6 [adp] => 687 [classroom guaranteed per delegate] => 1037 ) [7] => Array ( [remote guaranteed] => 6433 [classroom guaranteed] => 6972 [remote guaranteed per delegate] => 919 [delegates] => 7 [adp] => 687 [classroom guaranteed per delegate] => 996 ) [8] => Array ( [remote guaranteed] => 7120 [classroom guaranteed] => 7720 [remote guaranteed per delegate] => 890 [delegates] => 8 [adp] => 687 [classroom guaranteed per delegate] => 965 ) [9] => Array ( [remote guaranteed] => 7812 [classroom guaranteed] => 8460 [remote guaranteed per delegate] => 868 [delegates] => 9 [adp] => 687 [classroom guaranteed per delegate] => 940 ) [10] => Array ( [remote guaranteed] => 8500 [classroom guaranteed] => 9210 [remote guaranteed per delegate] => 850 [delegates] => 10 [adp] => 687 [classroom guaranteed per delegate] => 921 ) ) ) ) ) [ec_4967] => Array ( [tid] => ec_4967 [title] => Quito [sales_area] => ec_ecuador [venues] => Array ( [ec_15661447] => Array ( [vid] => ec_15661447 [title] => Quito - Av Eloy Alfaro [vfdc] => 200.00 [prices] => Array ( [1] => Array ( [remote guaranteed] => 2312 [classroom guaranteed] => 2512 [remote guaranteed per delegate] => 2312 [delegates] => 1 [adp] => 687 [classroom guaranteed per delegate] => 2512 ) [2] => Array ( [remote guaranteed] => 3000 [classroom guaranteed] => 3250 [remote guaranteed per delegate] => 1500 [delegates] => 2 [adp] => 687 [classroom guaranteed per delegate] => 1625 ) [3] => Array ( [remote guaranteed] => 3687 [classroom guaranteed] => 3987 [remote guaranteed per delegate] => 1229 [delegates] => 3 [adp] => 687 [classroom guaranteed per delegate] => 1329 ) [4] => Array ( [remote guaranteed] => 4372 [classroom guaranteed] => 4724 [remote guaranteed per delegate] => 1093 [delegates] => 4 [adp] => 687 [classroom guaranteed per delegate] => 1181 ) [5] => Array ( [remote guaranteed] => 5060 [classroom guaranteed] => 5460 [remote guaranteed per delegate] => 1012 [delegates] => 5 [adp] => 687 [classroom guaranteed per delegate] => 1092 ) [6] => Array ( [remote guaranteed] => 5748 [classroom guaranteed] => 6198 [remote guaranteed per delegate] => 958 [delegates] => 6 [adp] => 687 [classroom guaranteed per delegate] => 1033 ) [7] => Array ( [remote guaranteed] => 6433 [classroom guaranteed] => 6937 [remote guaranteed per delegate] => 919 [delegates] => 7 [adp] => 687 [classroom guaranteed per delegate] => 991 ) [8] => Array ( [remote guaranteed] => 7120 [classroom guaranteed] => 7672 [remote guaranteed per delegate] => 890 [delegates] => 8 [adp] => 687 [classroom guaranteed per delegate] => 959 ) [9] => Array ( [remote guaranteed] => 7812 [classroom guaranteed] => 8406 [remote guaranteed per delegate] => 868 [delegates] => 9 [adp] => 687 [classroom guaranteed per delegate] => 934 ) [10] => Array ( [remote guaranteed] => 8500 [classroom guaranteed] => 9150 [remote guaranteed per delegate] => 850 [delegates] => 10 [adp] => 687 [classroom guaranteed per delegate] => 915 ) ) ) ) ) ) [remote] => Array ( [1] => Array ( [remote guaranteed] => 2312 [remote guaranteed per delegate] => 2312 [adp] => 687 ) [2] => Array ( [remote guaranteed] => 3000 [remote guaranteed per delegate] => 1500 [adp] => 687 ) [3] => Array ( [remote guaranteed] => 3687 [remote guaranteed per delegate] => 1229 [adp] => 687 ) [4] => Array ( [remote guaranteed] => 4372 [remote guaranteed per delegate] => 1093 [adp] => 687 ) [5] => Array ( [remote guaranteed] => 5060 [remote guaranteed per delegate] => 1012 [adp] => 687 ) [6] => Array ( [remote guaranteed] => 5748 [remote guaranteed per delegate] => 958 [adp] => 687 ) [7] => Array ( [remote guaranteed] => 6433 [remote guaranteed per delegate] => 919 [adp] => 687 ) [8] => Array ( [remote guaranteed] => 7120 [remote guaranteed per delegate] => 890 [adp] => 687 ) [9] => Array ( [remote guaranteed] => 7812 [remote guaranteed per delegate] => 868 [adp] => 687 ) [10] => Array ( [remote guaranteed] => 8500 [remote guaranteed per delegate] => 850 [adp] => 687 ) ) [currency] => USD ) [5] => Array ( ) [6] => Array ( ) [7] => 0 [8] => 1 [9] => [10] => ) ) [6] => Array ( [file] => /apps/nobleprog-website/core/routes.php [line] => 19 [function] => course_menu_callback [args] => Array ( [0] => /en/cc/mlbigdata ) ) [7] => Array ( [file] => /apps/nobleprog-website/__index.php [line] => 100 [args] => Array ( [0] => /apps/nobleprog-website/core/routes.php ) [function] => require_once ) [8] => Array ( [file] => /apps/nobleprog-website/_index.php [line] => 26 [args] => Array ( [0] => /apps/nobleprog-website/__index.php ) [function] => include_once ) [9] => Array ( [file] => /apps/hitra7/index.php [line] => 54 [args] => Array ( [0] => /apps/nobleprog-website/_index.php ) [function] => include_once ) ) NP URI: www.nobleprog.com.ec/en/cc/mlbigdata Undefined array key "nobleprog_site_production_url" /apps/nobleprog-website/includes/functions/new-modules-general-functions.php:82 Array ( [0] => Array ( [file] => /apps/nobleprog-website/includes/functions/new-modules-general-functions.php [line] => 82 [function] => myErrorHandler [args] => Array ( [0] => 2 [1] => Undefined array key "nobleprog_site_production_url" [2] => /apps/nobleprog-website/includes/functions/new-modules-general-functions.php [3] => 82 ) ) [1] => Array ( [file] => /apps/hitra7/drupal7/sites/all/modules/_custom/frontend/islc7/islc7.module [line] => 131 [function] => variable_get [args] => Array ( [0] => nobleprog_site_production_url ) ) [2] => Array ( [file] => /apps/hitra7/drupal7/sites/all/modules/_custom/frontend/islc7/islc7_block.inc [line] => 44 [function] => islc_get_current_site [args] => Array ( ) ) [3] => Array ( [file] => /apps/nobleprog-website/nptemplates/default.php [line] => 265 [function] => islc7_sites_links_array_v3 [args] => Array ( ) ) [4] => Array ( [file] => /apps/nobleprog-website/modules/course/course.php [line] => 85 [args] => Array ( [0] => /apps/nobleprog-website/nptemplates/default.php ) [function] => require_once ) [5] => Array ( [file] => /apps/nobleprog-website/modules/course/course.php [line] => 31 [function] => course_render [args] => Array ( [0] => Array ( [course_code] => mlbigdata [hr_nid] => 318287 [title] => Machine Learning and Big Data [requirements] =>

Audience

[overview] =>

This instructor-led, live training (online or onsite) is aimed at technical persons who wish to learn how to implement a machine learning strategy while maximizing the use of big data.

By the end of this training, participants will:

Format of the Course

Course Customization Options

[category_overview] =>

This instructor-led, live training in <loc> (online or onsite) is aimed at technical persons who wish to learn how to implement a machine learning strategy while maximizing the use of big data.

By the end of this training, participants will:

[outline] =>

Introduction

History, Evolution and Trends for Machine Learning

The Role of Big Data in Machine Learning

Infrastructure for Managing Big Data

Using Historical and Real-time Data to Predict Behavior

Case Study: Machine Learning Across Industries

Evaluating Existing Applications and Capabilities

Upskilling for Machine Learning

Tools for Implementing Machine Learning

Cloud vs On-Premise Services

Understanding the Data Middle Backend

Overview of Data Mining and Analysis

Combining Machine Learning with Data Mining

Case Study: Deploying Intelligent Applications to Deliver Personalized Experiences to Users

Summary and Conclusion

[language] => en [duration] => 7 [status] => published [changed] => 1715350267 [source_title] => Machine Learning and Big Data [source_language] => en [cert_code] => [weight] => 0 [excluded_sites] => [use_mt] => stdClass Object ( [field_overview] => [field_course_outline] => [field_prerequisits] => [field_overview_in_category] => ) ) [1] => Array ( [0] => stdClass Object ( [tid] => 766 [alias] => big-data-training [name] => Big Data [english_name] => Big Data [consulting_option] => available_promoted ) [1] => stdClass Object ( [tid] => 943 [alias] => machine-learning-training [name] => Machine Learning [english_name] => Machine Learning [consulting_option] => ) ) [2] => mlbigdata [3] => Array ( [outlines] => Array ( [h2oautoml] => stdClass Object ( [course_code] => h2oautoml [hr_nid] => 306590 [title] => H2O AutoML [requirements] =>

Audience

[overview] =>

H2O AutoML is an artificial intelligence platform that automates the process of building, selecting and optimizing large numbers of machine learning models.

This instructor-led, live training (online or onsite) is aimed at data scientists who wish to use H2O AutoML to automate the process of building and selecting the best machine learning algorithm and parameters.

By the end of this training, participants will be able to:

Format of the Course

Course Customization Options

[category_overview] =>

This instructor-led, live training in <loc> (online or onsite) is aimed at data scientists who wish to use H2O AutoML to automoate the process of building and selecting the best machine learning algorithm and parameters.

By the end of this training, participants will be able to:

[outline] =>

Introduction

Setting up a Working Environment

Installing H2O

Anatomy of a Standard Machine Learning Workflow

Statistical and Machine Learning Algorithms

How H2O Automates the Machine Learning Workflow

Case Study: Predicting Product Availability

Downloading a Dataset

Building a Machine Learning Model

Specify a Training Frame

Training and Cross-Validating Different Models

Tuning the Hyperparameters

Training two Stacked Ensemble Models

Generating a Leaderboard of the Best Models

Inspecting the Ensemble Composition

Training many Deep Neural Network Models

Troubleshooting

Summary and Conclusion

[language] => en [duration] => 14 [status] => published [changed] => 1712161736 [source_title] => H2O AutoML [source_language] => en [cert_code] => [weight] => 0 [excluded_sites] => [use_mt] => stdClass Object ( [field_overview] => [field_course_outline] => [field_prerequisits] => [field_overview_in_category] => ) [cc] => h2oautoml ) [autosklearn] => stdClass Object ( [course_code] => autosklearn [hr_nid] => 306518 [title] => AutoML with Auto-sklearn [requirements] =>

Audience

[overview] =>

Auto-sklearn is a Python package built around the scikit-learn machine learning library. It automatically searches for the right learning algorithm for a new machine learning dataset and optimizes its parameters.

This instructor-led, live training (online or onsite) is aimed at machine learning practitioners who wish to use Auto-sklearn to automate the process of selecting and optimizing a machine learning model.

By the end of this training, participants will be able to:

Format of the Course

Course Customization Options

[category_overview] =>

This instructor-led, live training in <loc> (online or onsite) is aimed at machine learning practitioners who wish to use Auto-sklearn to automate the process of selecting and optimizing a machine learning model.

By the end of this training, participants will be able to:

[outline] =>

Introduction

Setting up a Working Environment

Installing Auto-sklearn

Anatomy of a Standard Machine Learning Workflow

How Auto-sklearn Automates the Machine Learning Workflow

Searching for the Best Neural Network Architecture with NAS (Neural Architecture Search)

Case Study: AutoML with Auto-sklearn

Downloading a Dataset

Building a Machine Learning Model

Training and Testing the Model

Tuning the Hyperparameters

Building, Training, and Testing Additional Models

Tweaking the Hyperparameters to Improve Accuracy

Configuring Auto-sklearn for Deep Learning Models

Troubleshooting

Summary and Conclusion

[language] => en [duration] => 14 [status] => published [changed] => 1712075696 [source_title] => AutoML with Auto-sklearn [source_language] => en [cert_code] => [weight] => 0 [excluded_sites] => [use_mt] => stdClass Object ( [field_overview] => [field_course_outline] => [field_prerequisits] => [field_overview_in_category] => ) [cc] => autosklearn ) [autokeras] => stdClass Object ( [course_code] => autokeras [hr_nid] => 306514 [title] => AutoML with Auto-Keras [requirements] =>

Audience

[overview] =>

Auto-Keras (Also known as Autokeras or Auto Keras) is an open source Python library for automated machine learning (AutoML).

This instructor-led, live training (online or onsite) is aimed at data scientists as well as less technical persons who wish to use Auto-Keras to automate the process of selecting and optimizing a machine learning model.

By the end of this training, participants will be able to:

Format of the Course

Course Customization Options

[category_overview] =>

This instructor-led, live training in <loc> (online or onsite) is aimed at data scientists as well as less technical persons who wish to use Auto-Keras to automate the process of selecting and optimizing a machine learning model.

By the end of this training, participants will be able to:

[outline] =>

Introduction

Setting up a Working Environment

Installing Auto-Keras

Anatomy of a Standard Machine Learning Workflow

How Auto-Keras Automates the Machine Learning Workflow

Searching for the Best Neural Network Architecture with NAS (Neural Architecture Search)

Case Study: AutoML with Auto-Keras

Downloading a Dataset

Building a Machine Learning Model

Training and Testing the Model

Tuning the Hyperparameters

Building, Training, and Testing Additional Models

Tweaking the Hyperparameters to Improve Accuracy

Configuring Auto-Keras for Deep Learning Models

Troubleshooting

Summary and Conclusion

[language] => en [duration] => 14 [status] => published [changed] => 1712161692 [source_title] => AutoML with Auto-Keras [source_language] => en [cert_code] => [weight] => 0 [excluded_sites] => [use_mt] => stdClass Object ( [field_overview] => [field_course_outline] => [field_prerequisits] => [field_overview_in_category] => ) [cc] => autokeras ) [advstablediffusion] => stdClass Object ( [course_code] => advstablediffusion [hr_nid] => 437195 [title] => Advanced Stable Diffusion: Deep Learning for Text-to-Image Generation [requirements] =>

Audience

[overview] =>

Stable Diffusion is a powerful deep learning model that can generate detailed images based on text descriptions. 

This instructor-led, live training (online or onsite) is aimed at intermediate to advanced-level data scientists, machine learning engineers, deep learning researchers, and computer vision experts who wish to expand their knowledge and skills in deep learning for text-to-image generation.

By the end of this training, participants will be able to:

Format of the Course

Course Customization Options

[category_overview] =>

This instructor-led, live training in <loc> (online or onsite) is aimed at intermediate to advanced-level data scientists, machine learning engineers, deep learning researchers, and computer vision experts who wish to expand their knowledge and skills in deep learning for text-to-image generation.

By the end of this training, participants will be able to:

[outline] =>

Introduction to Advanced Stable Diffusion

Advanced Text-to-Image Generation Techniques with Stable Diffusion

Performance Optimization and Scaling for Stable Diffusion

Hyperparameter Tuning and Generalization with Stable Diffusion

Integrating Stable Diffusion with Other Deep Learning Frameworks and Tools

Debugging and Troubleshooting Stable Diffusion Models

Summary and Next Steps

[language] => en [duration] => 21 [status] => published [changed] => 1712162957 [source_title] => Advanced Stable Diffusion: Deep Learning for Text-to-Image Generation [source_language] => en [cert_code] => [weight] => -1002 [excluded_sites] => [use_mt] => stdClass Object ( [field_overview] => [field_course_outline] => [field_prerequisits] => [field_overview_in_category] => ) [cc] => advstablediffusion ) [stablediffusion] => stdClass Object ( [course_code] => stablediffusion [hr_nid] => 437191 [title] => Introduction to Stable Diffusion for Text-to-Image Generation [requirements] =>

Audience

[overview] =>

Stable Diffusion is a powerful deep learning model that can generate detailed images based on text descriptions. 

This instructor-led, live training (online or onsite) is aimed at data scientists, machine learning engineers, and computer vision researchers who wish to leverage Stable Diffusion to generate high-quality images for a variety of use cases.

By the end of this training, participants will be able to:

Format of the Course

Course Customization Options

[category_overview] =>

This instructor-led, live training in (online or onsite) is aimed at data scientists, machine learning engineers, and computer vision researchers who wish to leverage Stable Diffusion to generate high-quality images for a variety of use cases.

By the end of this training, participants will be able to:

[outline] =>

Introduction to Stable Diffusion

Building Stable Diffusion Models

Advanced Stable Diffusion Techniques

Optimizing Stable Diffusion Models

Case Studies and Best Practices

Summary and Next Steps

[language] => en [duration] => 21 [status] => published [changed] => 1712157732 [source_title] => Introduction to Stable Diffusion for Text-to-Image Generation [source_language] => en [cert_code] => [weight] => -1001 [excluded_sites] => [use_mt] => stdClass Object ( [field_overview] => [field_course_outline] => [field_prerequisits] => [field_overview_in_category] => ) [cc] => stablediffusion ) [alphafold] => stdClass Object ( [course_code] => alphafold [hr_nid] => 396067 [title] => AlphaFold [requirements] =>

Audience

[overview] =>

AlphaFold is an Artificial Intelligence (AI) system that performs the prediction of protein structures. It is developed by Alphabet’s/Google’s DeepMind as a deep learning system that can accurately predict 3D models of protein structures.

This instructor-led, live training (online or onsite) is aimed at biologists who wish to understand how AlphaFold works and use AlphaFold models as guides in their experimental studies.

By the end of this training, participants will be able to:

Format of the Course

Course Customization Options

[category_overview] =>

This instructor-led, live training in <loc> (online or onsite) is aimed at biologists who wish to understand how AlphaFold works and use AlphaFold models as guides in their experimental studies.

By the end of this training, participants will be able to:

[outline] =>

Introduction to AlphaFold

How Does AlphaFold Work?

Accessing AlphaFold

AlphaFold Protein Structure Database

AlphaFold Colab

AlphaFold Open Source Code

Interpreting AlphaFold Predictions

AlphaFold Use Cases

Summary and Next Steps

[language] => en [duration] => 7 [status] => published [changed] => 1700037846 [source_title] => AlphaFold [source_language] => en [cert_code] => [weight] => -1001 [excluded_sites] => [use_mt] => stdClass Object ( [field_overview] => [field_course_outline] => [field_prerequisits] => [field_overview_in_category] => ) [cc] => alphafold ) [tensorflowlite] => stdClass Object ( [course_code] => tensorflowlite [hr_nid] => 341007 [title] => TensorFlow Lite for Embedded Linux [requirements] =>

Audience

[overview] =>

TensorFlow Lite is an open source deep learning framework for executing models on mobile and embedded devices with limited compute and memory resources.

This instructor-led, live training (online or onsite) is aimed at developers who wish to use TensorFlow Lite to deploy deep learning models on embedded devices.

By the end of this training, participants will be able to:

Format of the Course

Course Customization Options

[category_overview] =>

This instructor-led, live training in <loc> (online or onsite) is aimed at developers who wish to use TensorFlow Lite to deploy deep learning models on embedded devices.

By the end of this training, participants will be able to:

[outline] =>

Introduction

Overview of TensorFlow Lite Features and Operations

Setting up TensorFlow Lite

Choosing a Model to Run on a Device

Customizing a Pre-trained Model

Converting a Model

Running a Prediction Model

Accelerating Model Operations

Adding Model Operations

Optimizing the Model

Troubleshooting

Summary and Conclusion

[language] => en [duration] => 21 [status] => published [changed] => 1700037625 [source_title] => TensorFlow Lite for Embedded Linux [source_language] => en [cert_code] => [weight] => 0 [excluded_sites] => [use_mt] => stdClass Object ( [field_overview] => [field_course_outline] => [field_prerequisits] => [field_overview_in_category] => ) [cc] => tensorflowlite ) [tensorflowliteandroid] => stdClass Object ( [course_code] => tensorflowliteandroid [hr_nid] => 341059 [title] => TensorFlow Lite for Android [requirements] =>

Audience

[overview] =>

TensorFlow Lite is an open source deep learning framework for mobile devices and embedded systems.

This instructor-led, live training (online or onsite) is aimed at developers who wish to use TensorFlow Lite to develop mobile applications with deep learning capabilities.

By the end of this training, participants will be able to:

Format of the Course

Course Customization Options

[category_overview] =>

This instructor-led, live training in <loc> (online or onsite) is aimed at developers who wish to use TensorFlow Lite to develop mobile applications with deep learning capabilities.

By the end of this training, participants will be able to:

[outline] =>

Introduction

Overview of TensorFlow Lite Features and Design

Machine Learning and Deep Learning Fundamentals

Preparing the Mobile App Development Environment

Creating an App for Object Recognition

Setting up TensorFlow Lite

Selecting a TensorFlow Model

Converting the TensorFlow Model

Loading the TensorFlow Model onto a Mobile Device

Optimizing the TensorFlow Model for Mobile Devices

Adding Chat Capabilities for Smarter Replies

Loading a Pre-trained TensorFlow Model

Retraining a TensorFlow Model

Pre-processing a Dataset

Setting the Hyperparameters

Deploying the AI Enabled App

Running TensorFlow Models on Other Embedded Devices

Troubleshooting

Summary and Conclusion

[language] => en [duration] => 21 [status] => published [changed] => 1700037626 [source_title] => TensorFlow Lite for Android [source_language] => en [cert_code] => [weight] => 0 [excluded_sites] => [use_mt] => stdClass Object ( [field_overview] => [field_course_outline] => [field_prerequisits] => [field_overview_in_category] => ) [cc] => tensorflowliteandroid ) [tensorflowliteios] => stdClass Object ( [course_code] => tensorflowliteios [hr_nid] => 341271 [title] => TensorFlow Lite for iOS [requirements] =>

Audience

[overview] =>

TensorFlow Lite is an open source deep learning framework for mobile devices and embedded systems.

This instructor-led, live training (online or onsite) is aimed at developers who wish to use TensorFlow Lite to develop iOS mobile applications with deep learning capabilities.

By the end of this training, participants will be able to:

Format of the Course

Course Customization Options

[category_overview] =>

This instructor-led, live training in (online or onsite) is aimed at developers who wish to use TensorFlow Lite to develop iOS mobile applications with deep learning capabilities.

By the end of this training, participants will be able to:

[outline] =>

Introduction

Overview of TensorFlow Lite Features and Workflow

Preparing the Development Environment

Capturing an Image with a Device Camera

Creating an App for Object Detection

Creating an App for Image Classification

Customizing the Model and Data

Optimizing the TensorFlow Model

Exploring Alternative Models

Deploying the AI Enabled iOS App

Troubleshooting

Summary and Conclusion

[language] => en [duration] => 21 [status] => published [changed] => 1700037628 [source_title] => TensorFlow Lite for iOS [source_language] => en [cert_code] => [weight] => 0 [excluded_sites] => [use_mt] => stdClass Object ( [field_overview] => [field_course_outline] => [field_prerequisits] => [field_overview_in_category] => ) [cc] => tensorflowliteios ) [tensorflowlitemicro] => stdClass Object ( [course_code] => tensorflowlitemicro [hr_nid] => 341211 [title] => Tensorflow Lite for Microcontrollers [requirements] =>

Audience

[overview] =>

TensorFlow Lite for Microcontrollers is a port of TensorFlow Lite designed to run machine learning models on microcontrollers and other devices with limited memory.

This instructor-led, live training (online or onsite) is aimed at engineers who wish to write, load and run machine learning models on very small embedded devices.

By the end of this training, participants will be able to:

Format of the Course

Course Customization Options

[category_overview] =>

This instructor-led, live training in <loc> (online or onsite) is aimed at engineers who wish to write, load and run machine learning models on very small embedded devices.

By the end of this training, participants will be able to:

[outline] =>

Introduction

Overview of TensorFlow Lite Features

Constraints of a Microcontroller

Getting Started

Creating an Audio Detection System

Serializing the Code

Working with Microcontroller'ss C++ Libraries

Verifying the Results

Creating an Image Detection System

Deploying an AI-enabled Device

Troubleshooting

Summary and Conclusion

[language] => en [duration] => 21 [status] => published [changed] => 1700037627 [source_title] => Tensorflow Lite for Microcontrollers [source_language] => en [cert_code] => [weight] => 0 [excluded_sites] => [use_mt] => stdClass Object ( [field_overview] => [field_course_outline] => [field_prerequisits] => [field_overview_in_category] => ) [cc] => tensorflowlitemicro ) [chainer] => stdClass Object ( [course_code] => chainer [hr_nid] => 372843 [title] => Deep Learning Neural Networks with Chainer [requirements] =>

Audience

[overview] =>

Chainer is an open source framework based on Python, built for accelerating research and implementing neural network models. It provides flexible, efficient, and simplified approaches to developing deep learning algorithms.

This instructor-led, live training (online or onsite) is aimed at researchers and developers who wish to use Chainer to build and train neural networks in Python while making the code easy to debug.

By the end of this training, participants will be able to:

Format of the Course

Course Customization Options

[category_overview] =>

This instructor-led, live training in <loc> (online or onsite) is aimed at researchers and developers who wish to use Chainer to build and train neural networks in Python while making the code easy to debug.

By the end of this training, participants will be able to:

[outline] =>

Introduction

Getting Started

Training Neural Networks in Chainer

Working with GPUs in Chainer

Implementing Other Neural Network Models

Troubleshooting

Summary and Conclusion

[language] => en [duration] => 14 [status] => published [changed] => 1700037772 [source_title] => Deep Learning Neural Networks with Chainer [source_language] => en [cert_code] => [weight] => 0 [excluded_sites] => [use_mt] => stdClass Object ( [field_overview] => [field_course_outline] => [field_prerequisits] => [field_overview_in_category] => ) [cc] => chainer ) [horovod] => stdClass Object ( [course_code] => horovod [hr_nid] => 372747 [title] => Distributed Deep Learning with Horovod [requirements] =>

Audience

[overview] =>

Horovod is an open source software framework, designed for processing fast and efficient distributed deep learning models using TensorFlow, Keras, PyTorch, and Apache MXNet. It can scale up a single-GPU training script to run on multiple GPUs or hosts with minimal code changes.

This instructor-led, live training (online or onsite) is aimed at developers or data scientists who wish to use Horovod to run distributed deep learning trainings and scale it up to run across multiple GPUs in parallel.

By the end of this training, participants will be able to:

Format of the Course

Course Customization Options

[category_overview] =>

This instructor-led, live training in <loc> (online or onsite) is aimed at developers or data scientists who wish to use Horovod to run distributed deep learning trainings and scale it up to run across multiple GPUs in parallel.

By the end of this training, participants will be able to:

[outline] =>

Introduction

Installing and Configuring Horovod

Running Distributed Training

Optimizing Distributed Training Processes

Troubleshooting

Summary and Conclusion

[language] => en [duration] => 7 [status] => published [changed] => 1700037772 [source_title] => Distributed Deep Learning with Horovod [source_language] => en [cert_code] => [weight] => 0 [excluded_sites] => [use_mt] => stdClass Object ( [field_overview] => [field_course_outline] => [field_prerequisits] => [field_overview_in_category] => ) [cc] => horovod ) [fpgaopenvino] => stdClass Object ( [course_code] => fpgaopenvino [hr_nid] => 339527 [title] => Accelerating Deep Learning with FPGA and OpenVINO [requirements] =>

Audience

[overview] =>

An FPGA (Field Programmable Gate Array) is an integrated circuit that can be used to accelerate deep learning computations. OpenVINO is an open source toolkit for optimizing Deep Learning models on Intel hardware.

This instructor-led, live training (online or onsite) is aimed at data scientists who wish to accelerate real-time machine learning applications and deploy them at scale.

By the end of this training, participants will be able to:

Format of the Course

Course Customization Options

[category_overview] =>

This instructor-led, live training in <loc> (online or onsite) is aimed at data scientists who wish to accelerate real-time machine learning applications and deploy them at scale.

By the end of this training, participants will be able to:

[outline] =>

Introduction

Overview the Languages, Tools, and Libraries Needed for Accelerating a Computer Vision Application

Setting up OpenVINO

Overview of OpenVINO Toolkit and its Components

Understanding Deep Learning Acceleration GPU and FPGA

Writing Software That Targets FPGA

Converting a Model Format for an Inference Engine

Mapping Network Topologies onto FPGA Architecture

Using an Acceleration Stack to Enable an FPGA Cluster

Setting up an Application to Discover an FPGA Accelerator

Deploying the Application for Real World Image Recognition

Troubleshooting

Summary and Conclusion

[language] => en [duration] => 35 [status] => published [changed] => 1700037620 [source_title] => Accelerating Deep Learning with FPGA and OpenVINO [source_language] => en [cert_code] => [weight] => 0 [excluded_sites] => [use_mt] => stdClass Object ( [field_overview] => [field_course_outline] => [field_prerequisits] => [field_overview_in_category] => ) [cc] => fpgaopenvino ) [mxnet] => stdClass Object ( [course_code] => mxnet [hr_nid] => 339519 [title] => Building Deep Learning Models with Apache MXNet [requirements] =>

Audience

[overview] =>

MXNet is a flexible, open-source Deep Learning library that is popular for research prototyping and production. Together with the high-level Gluon API interface, Apache MXNet is a powerful alternative to TensorFlow and PyTorch.

This instructor-led, live training (online or onsite) is aimed at data scientists who wish to use Apache MXNet to build and deploy a deep learning model for image recognition.

By the end of this training, participants will be able to:

Format of the Course

Course Customization Options

[category_overview] =>

This instructor-led, live training in (online or onsite) is aimed at data scientists who wish to use Apache MXNet's to build and deploy a deep learning model for image recognition.

By the end of this training, participants will be able to:

[outline] =>

Introduction

Deep Learning Principles and the Deep Learning Ecosystem

Overview of Apache MXNet Features and Architecture

Setup

Working with Data

Developing a Deep Learning Model

Deploying the Model

MXNet Security Best Practices

Troubleshooting

Summary and Conclusion

[language] => en [duration] => 21 [status] => published [changed] => 1700037620 [source_title] => Building Deep Learning Models with Apache MXNet [source_language] => en [cert_code] => [weight] => 0 [excluded_sites] => [use_mt] => stdClass Object ( [field_overview] => [field_course_outline] => [field_prerequisits] => [field_overview_in_category] => ) [cc] => mxnet ) [keras] => stdClass Object ( [course_code] => keras [hr_nid] => 314371 [title] => Deep Learning with Keras [requirements] =>

Audience

[overview] =>

Keras is a high-level neural networks API for fast development and experimentation. It runs on top of TensorFlow, CNTK, or Theano.

This instructor-led, live training (online or onsite) is aimed at technical persons who wish to apply deep learning model to image recognition applications.

By the end of this training, participants will be able to:

Format of the Course

Course Customization Options

[category_overview] =>

This instructor-led, live training in <loc> (online or onsite) is aimed at technical persons who wish to apply deep learning model to image recognition applications.

By the end of this training, participants will be able to:

[outline] =>

Introduction

Overview of Neural Networks

Understanding Convolutional Networks

Setting up Keras

Overview of Keras Features and Architecture

Overview of Keras Syntax

Understanding How a Keras Model Organize Layers

Configuring the Keras Backend (TensorFlow or Theano)

Implementing an Unsupervised Learning Model

Analyzing Images with a Convolutional Neural Network (CNN)

Preprocessing Data

Training the Model

Training on CPU vs GPU vs TPU

Evaluating the Model

Using a Pre-trained Deep Learning Model

Setting up a Recurrent Neural Network (RNN)

Debugging the Model

Saving the Model

Deploying the Model

Monitoring a Keras Model with TensorBoard

Troubleshooting

Summary and Conclusion

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Av Eloy Alfaro [vfdc] => 200.00 [prices] => Array ( [1] => Array ( [remote guaranteed] => 2312 [classroom guaranteed] => 2512 [remote guaranteed per delegate] => 2312 [delegates] => 1 [adp] => 687 [classroom guaranteed per delegate] => 2512 ) [2] => Array ( [remote guaranteed] => 3000 [classroom guaranteed] => 3250 [remote guaranteed per delegate] => 1500 [delegates] => 2 [adp] => 687 [classroom guaranteed per delegate] => 1625 ) [3] => Array ( [remote guaranteed] => 3687 [classroom guaranteed] => 3987 [remote guaranteed per delegate] => 1229 [delegates] => 3 [adp] => 687 [classroom guaranteed per delegate] => 1329 ) [4] => Array ( [remote guaranteed] => 4372 [classroom guaranteed] => 4724 [remote guaranteed per delegate] => 1093 [delegates] => 4 [adp] => 687 [classroom guaranteed per delegate] => 1181 ) [5] => Array ( [remote guaranteed] => 5060 [classroom guaranteed] => 5460 [remote guaranteed per delegate] => 1012 [delegates] => 5 [adp] => 687 [classroom guaranteed per delegate] => 1092 ) [6] => Array ( [remote guaranteed] => 5748 [classroom guaranteed] => 6198 [remote guaranteed per delegate] => 958 [delegates] => 6 [adp] => 687 [classroom guaranteed per delegate] => 1033 ) [7] => Array ( [remote guaranteed] => 6433 [classroom guaranteed] => 6937 [remote guaranteed per delegate] => 919 [delegates] => 7 [adp] => 687 [classroom guaranteed per delegate] => 991 ) [8] => Array ( [remote guaranteed] => 7120 [classroom guaranteed] => 7672 [remote guaranteed per delegate] => 890 [delegates] => 8 [adp] => 687 [classroom guaranteed per delegate] => 959 ) [9] => Array ( [remote guaranteed] => 7812 [classroom guaranteed] => 8406 [remote guaranteed per delegate] => 868 [delegates] => 9 [adp] => 687 [classroom guaranteed per delegate] => 934 ) [10] => Array ( [remote guaranteed] => 8500 [classroom guaranteed] => 9150 [remote guaranteed per delegate] => 850 [delegates] => 10 [adp] => 687 [classroom guaranteed per delegate] => 915 ) ) ) ) ) ) [remote] => Array ( [1] => Array ( [remote guaranteed] => 2312 [remote guaranteed per delegate] => 2312 [adp] => 687 ) [2] => Array ( [remote guaranteed] => 3000 [remote guaranteed per delegate] => 1500 [adp] => 687 ) [3] => Array ( [remote guaranteed] => 3687 [remote guaranteed per delegate] => 1229 [adp] => 687 ) [4] => Array ( [remote guaranteed] => 4372 [remote guaranteed per delegate] => 1093 [adp] => 687 ) [5] => Array ( [remote guaranteed] => 5060 [remote guaranteed per delegate] => 1012 [adp] => 687 ) [6] => Array ( [remote guaranteed] => 5748 [remote guaranteed per delegate] => 958 [adp] => 687 ) [7] => Array ( [remote guaranteed] => 6433 [remote guaranteed per delegate] => 919 [adp] => 687 ) [8] => Array ( [remote guaranteed] => 7120 [remote guaranteed per delegate] => 890 [adp] => 687 ) [9] => Array ( [remote guaranteed] => 7812 [remote guaranteed per delegate] => 868 [adp] => 687 ) [10] => Array ( [remote guaranteed] => 8500 [remote guaranteed per delegate] => 850 [adp] => 687 ) ) [currency] => USD ) [5] => Array ( ) [6] => Array ( ) [7] => 0 [8] => 1 [9] => [10] => ) ) [6] => Array ( [file] => /apps/nobleprog-website/core/routes.php [line] => 19 [function] => course_menu_callback [args] => Array ( [0] => /en/cc/mlbigdata ) ) [7] => Array ( [file] => /apps/nobleprog-website/__index.php [line] => 100 [args] => Array ( [0] => /apps/nobleprog-website/core/routes.php ) [function] => require_once ) [8] => Array ( [file] => /apps/nobleprog-website/_index.php [line] => 26 [args] => Array ( [0] => /apps/nobleprog-website/__index.php ) [function] => include_once ) [9] => Array ( [file] => /apps/hitra7/index.php [line] => 54 [args] => Array ( [0] => /apps/nobleprog-website/_index.php ) [function] => include_once ) )