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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