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Machine Learning for Banking (with Python) Training Course

Machine Learning is a branch of Artificial Intelligence wherein computers have the ability to learn without being explicitly programmed. Python is a programming language famous for its clear syntax and readability. It offers an excellent collection of well-tested libraries and techniques for developing machine learning applications.

In this instructor-led, live training, participants will learn how to apply machine learning techniques and tools for solving real-world problems in the banking industry.

Participants first learn the key principles, then put their knowledge into practice by building their own machine learning models and using them to complete a number of team projects.

Audience

Developers
Data scientists

Format of the course

Part lecture, part discussion, exercises and heavy hands-on practice

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Course Outline

Introduction

Difference between statistical learning (statistical analysis) and machine learning
Adoption of machine learning technology and talent by finance and banking companies

Different Types of Machine Learning

Supervised learning vs unsupervised learning
Iteration and evaluation
Bias-variance trade-off
Combining supervised and unsupervised learning (semi-supervised learning)

Machine Learning Languages and Toolsets

Open source vs proprietary systems and software
Python vs R vs Matlab
Libraries and frameworks

Machine Learning Case Studies

Consumer data and big data
Assessing risk in consumer and business lending
Improving customer service through sentiment analysis
Detecting identity fraud, billing fraud and money laundering

Hands-on: Python for Machine Learning

Preparing the Development Environment
Obtaining Python machine learning libraries and packages
Working with scikit-learn and PyBrain

How to Load Machine Learning Data

Databases, data warehouses and streaming data
Distributed storage and processing with Hadoop and Spark
Exported data and Excel

Modeling Business Decisions with Supervised Learning

Classifying your data (classification)
Using regression analysis to predict outcome
Choosing from available machine learning algorithms
Understanding decision tree algorithms
Understanding random forest algorithms
Model evaluation
Exercise

Regression Analysis

Linear regression
Generalizations and Nonlinearity
Exercise

Classification

Bayesian refresher
Naive Bayes
Logistic regression
K-Nearest neighbors
Exercise

Hands-on: Building an Estimation Model

Assessing lending risk based on customer type and history

Evaluating the performance of Machine Learning Algorithms

Cross-validation and resampling
Bootstrap aggregation (bagging)
Exercise

Modeling Business Decisions with Unsupervised Learning

When sample data sets are not available
K-means clustering
Challenges of unsupervised learning
Beyond K-means
Bayes networks and Markov Hidden Models
Exercise

Hands-on: Building a Recommendation System

Analyzing past customer behavior to improve new service offerings

Extending your company's capabilities

Developing models in the cloud
Accelerating machine learning with GPU
Applying Deep Learning neural networks for computer vision, voice recognition and text analysis

Closing Remarks

Requirements

Experience with Python programming
Basic familiarity with statistics and linear algebra

21 Hours

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