Hands-On Machine Learning with R

Hands-On Machine Learning with R
  • eBook:
    Hands-On Machine Learning with R
  • Author:
    Brad Boehmke, Brandon M. Greenwell
  • Edition:
    1 edition
  • Categories:
  • Data:
    November 11, 2019
  • ISBN:
  • ISBN-13:
  • Language:
  • Pages:
    488 pages
  • Format:
    PDF, ePUB

Book Description
Hands-on Machine Learning with R provides a practical and applied approach to learning and developing intuition into today’s most popular machine learning methods. This book serves as a practitioner’s guide to the machine learning process and is meant to help the reader learn to apply the machine learning stack within R, which includes using various R packages such as glmneth2orangerxgboostkeras, and others to effectively model and gain insight from their data. The book favors a hands-on approach, providing an intuitive understanding of machine learning concepts through concrete examples and just a little bit of theory. 
Throughout this book, the reader will be exposed to the entire machine learning process including feature engineering, resampling, hyperparameter tuning, model evaluation, and interpretation. The reader will be exposed to powerful algorithms such as regularized regression, random forests, gradient boosting machines, deep learning, generalized low rank models, and more! By favoring a hands-on approach and using real word data, the reader will gain an intuitive understanding of the architectures and engines that drive these algorithms and packages, understand when and how to tune the various hyperparameters, and be able to interpret model results. By the end of this book, the reader should have a firm grasp of R’s machine learning stack and be able to implement a systematic approach for producing high quality modeling results.
· Offers a practical and applied introduction to the most popular machine learning methods.
· Topics covered include feature engineering, resampling, deep learning and more.
· Uses a hands-on approach and real world data.


I - Fundamentals
1. Introduction to Machine Learning
2. Modeling Process
3. Feature & Target Engineering

II - Supervised Learning
4. Linear Regression
5. Logistic Regression
6. Regularized Regression
7. Multivariate Adaptive Regression Splines
8. K-Nearest Neighbors
9. Decision Trees
10. Bagging
11. Random Forests
12. Gradient Boosting
13. Deep Learning
14. Support Vector Machines
15. Stacked Models
16. Interpretable Machine Learning

III - Dimension Reduction
17. Principal Components Analysis
18. Generalized Low Rank Models
19. Autoencoders

IV - Clustering
20. K-means Clustering
21. Hierarchical Clustering
22. Model-based Clustering

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