coverpage
Mastering Machine Learning with R
Credits
About the Author
About the Reviewers
www.PacktPub.com
Support files eBooks discount offers and more
Preface
Machine learning defined
Machine learning caveats
Failure to engineer features
Overfitting and underfitting
Causality
What this book covers
What you need for this book
Who this book is for
Conventions
Reader feedback
Customer support
Chapter 1. A Process for Success
The process
Business understanding
Data understanding
Data preparation
Modeling
Evaluation
Deployment
Algorithm flowchart
Summary
Chapter 2. Linear Regression – The Blocking and Tackling of Machine Learning
Univariate linear regression
Multivariate linear regression
Other linear model considerations
Summary
Chapter 3. Logistic Regression and Discriminant Analysis
Classification methods and linear regression
Logistic regression
Model selection
Summary
Chapter 4. Advanced Feature Selection in Linear Models
Regularization in a nutshell
Business case
Modeling and evaluation
Model selection
Summary
Chapter 5. More Classification Techniques – K-Nearest Neighbors and Support Vector Machines
K-Nearest Neighbors
Support Vector Machines
Business case
Feature selection for SVMs
Summary
Chapter 6. Classification and Regression Trees
Introduction
An overview of the techniques
Business case
Summary
Chapter 7. Neural Networks
Neural network
Deep learning a not-so-deep overview
Business understanding
Data understanding and preparation
Modeling and evaluation
An example of deep learning
Summary
Chapter 8. Cluster Analysis
Hierarchical clustering
K-means clustering
Gower and partitioning around medoids
Data understanding and preparation
Modeling and evaluation
Summary
Chapter 9. Principal Components Analysis
An overview of the principal components
Modeling and evaluation
Summary
Chapter 10. Market Basket Analysis and Recommendation Engines
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
Summary
Chapter 11. Time Series and Causality
Univariate time series analysis
Modeling and evaluation
Summary
Chapter 12. Text Mining
Text mining framework and methods
Topic models
Modeling and evaluation
Summary
Appendix A. R Fundamentals
Introduction
Getting R up and running
Using R
Data frames and matrices
Summary stats
Installing and loading the R packages
Summary
Index
更新时间：2021-07-09 21:28:39
完结共103章
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