Duration
28 hours (usually 4 days including breaks)
Requirements
- Programming experience with any language
- Basic familiarity with statistics and linear algebra
Overview
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. R will be used as the programming language.
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 live projects.
Audience
- Developers
- Data scientists
- Banking professionals with a technical background
Format of the course
- Part lecture, part discussion, exercises and heavy hands-on practice
Course Outline
Introduction
- Difference between statistical learning (statistical analysis) and machine learning
- Adoption of machine learning technology 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
- R vs Python 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
Introduction to R
- Installing the RStudio IDE
- Loading R packages
- Data structures
- Vectors
- Factors
- Lists
- Data Frames
- Matrixes and Arrays
How to Load Machine Learning Data
- Databases, data warehouses and streaming data
- Distributed storage and processing with Hadoop and Spark
- Importing data from a database
- Importing data from Excel and CSV
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 additional GPUs
- Applying Deep Learning neural networks for computer vision, voice recognition and text analysis
Closing Remarks
