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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
