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
Testimonials
the group activity
Innovative Investors & Financing Co., Inc.; Innovative Investors & Financing Co., Inc.
Credit Risk Management for Consumer Lending Course
CAMPARI / TRUST FORMULA
VIRNA INTAL - Innovative Investors & Financing Co., Inc.
Credit Risk Management for Consumer Lending Course
I like the application cases wherein every topic, she has exercises to apply what we have learned.
CRISTINA MEDINA - Innovative Investors & Financing Co., Inc.
Credit Risk Management for Consumer Lending Course
The whole day was just great and relaxed which really helped. All the printouts really helped.
Louise Mackrell
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The lecturer is very knowledgeable and can substantiate theories with his own personal experiences.
Harry Estipona
Financial Markets Course
real exemples of the trainer
Joana Gomes
Compliance and the Management of Compliance Risk Course
She was very personable and presented a fluid delivery of the course material.
Karli Buckle
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Vajitha was engaging, very knowledgeable, clear and prepared
Cécile McNeil
Corporate Governance Course
Trainer, Richard was very good