This book is for data science professionals, data analysts, or anyone with working knowledge of machine learning with R, who now want to take their skills to the next level and become an expert in the field.
- 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
Who this book is for
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