We will now get to an overview of the techniques, covering the regression and classification trees, random forests, and gradient boosting. This will set the stage for the practical business cases.
- Preface
- A Process for Success
- Linear Regression - The Blocking and Tackling of Machine Learning
- Logistic Regression and Discriminant Analysis
- Advanced Feature Selection in Linear Models
- More Classification Techniques - K-Nearest Neighbors and Support Vector Machines
- Classification and Regression Trees
- Neural Networks and Deep Learning
- Cluster Analysis
- Principal Components Analysis
- Market Basket Analysis, Recommendation Engines, and Sequential Analysis
- An overview of a market basket analysis
- Business understanding
- Data understanding and preparation
- Modeling and evaluation
- An overview of a recommendation engine
- Business understanding and recommendations
- Data understanding, preparation, and recommendations
- Modeling, evaluation, and recommendations
- Sequential data analysis
- Summary
- Creating Ensembles and Multiclass Classification
- Time Series and Causality
- Text Mining
- R on the Cloud
- R Fundamentals
- Sources
An overview of the techniques
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