Art of Machine Learning

Learn to expertly apply a range of machine learning methods to real data with this practical guide.

Machine learning without advanced math! This book presents a serious, practical look at machine learning, preparing you for valuable insights on your own data. The Art of Machine Learning is packed with real dataset examples and sophisticated advice on how to make full use of powerful machine learning methods. Readers will need only an intuitive grasp of charts, graphs, and the slope of a line, as well as familiarity with the R programming language.

You’ll become skilled in a range of machine learning methods, starting with the simple k-Nearest Neighbors method (k-NN), then on to random forests, gradient boosting, linear/logistic models, support vector machines, the LASSO, and neural networks. Final chapters introduce text and image classification, as well as time series. You’ll learn not only how to use machine learning methods, but also why these methods work, providing the strong foundational background you’ll need in practice.

Additional features:

• How to avoid common problems, such as dealing with “dirty” data and factor variables with large numbers of levels
• A look at typical misconceptions, such as dealing with unbalanced data
• Exploration of the famous Bias-Variance Tradeoff, central to machine learning, and how it plays out in practice for each machine learning method
• Dozens of illustrative examples involving real datasets of varying size and field of application
• Standard R packages are used throughout, with a simple wrapper interface to provide convenient access.

After finishing this book, you will be well equipped to start applying machine learning techniques to your own datasets.

About the Author

Norman Matloff is an award-winning teacher at UC Davis, with a PhD in Mathematics from UCLA. He is the author of a number of books in the data science area, and his software and web tutorials are used all over the world. His book, Statistical Regression and Classification: from Linear Models to Machine Learning, was the recipient of the 2017 Ziegel Award, given by the prominent technical journal Technometrics. Matloff is frequently asked to give keynote addresses at data science conferences and he also writes about social issues. He was the recipient of the Distinguished Public Service Award from UC Davis and is also the author of The Art of Debugging with GDB, DDD, and Eclipse and The Art of R Programming (both No Starch Press).

Table of contents
Acknowledgments
Introduction
PART I: PROLOGUE, AND NEIGHBORHOOD-BASED METHODS
Chapter 1: Regression Models
Chapter 2: Classification Models
Chapter 3: Bias, Variance, Overfitting, and Cross-Validation
Chapter 4: Dealing with Large Numbers of Features
PART II: TREE-BASED METHODS
Chapter 5: A Step Beyond k-NN: Decision Trees
Chapter 6: Tweaking the Trees: Bagging, Random Forests, and Boosting
Chapter 7: Finding a Good Set of Hyperparameters
PART III: METHODS BASED ON LINEAR RELATIONSHIPS
Chapter 8: Parametric Methods: Linear and Generalized Linear Models
Chapter 9: Cutting Things Down to Size: Regularization
PART IV: METHODS BASED ON SEPARATING LINES AND PLANES
Chapter 10: A Boundary Approach: Support Vector Machines
Chapter 11: Linear Models on Steroids: Neural Networks
PART V: APPLICATIONS
Chapter 12: Image Classification
Chapter 13: Handling Time Series and Text Data
Appendix A: List of Acronyms and Symbols
Appendix B: Statistics–Machine Learning Terminology Correspondence
Appendix C: Matrices, Data Frames, and Factor Conversions
Appendix D: Pitfall: Beware of “p-Hacking”!