- Experience with Python programming
- Basic familiarity with statistics and linear algebra
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. Deep learning techniques are covered in the latter part of the course. Python 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 live team projects.
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
- K-means clustering
- Challenges of unsupervised learning
- Beyond K-means
- Exercise
Hands-on: Building a Recommendation System
- Analyzing past customer behavior to improve new service offerings
Introduction to Neural Networks and Deep Learning
- Layers and nodes
- Convolutional neural networks
- Recurrent neural networks
- Multilayer perceptrons
- Frameworks: Theano, TensorFlow, Keras
- Exercise
Hands-on: Building an AI system
- Monitoring big data to detect money laundering and billing fraud
Extending your company's capabilities
- Developing models in the cloud
- Accelerating machine learning with GPU
- Beyond machine learning: Artificial Intelligence (AI)
- Applying neural networks for computer vision, voice recognition and text analysis
Closing Remarks