The Art of Machine Learning Cover

The Art of Machine Learning

by Norman Matloff
April 2022, 250 pp.

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Machine learning for the rest of us! No fancy math is used in the book, yet it is a serious, practical look at the subject, preparing you for valuable insights on your own data. Using the R programming language, it contains few equations, but is packed with real dataset examples and sophisticated advice on how to make full use of powerful machine learning methods.

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, and time series. You’ll encounter dozens of illustrative examples, involving real data. You’ll learn not only how to use machine learning methods, but also why these methods work, providing a strong groundwork for your own practical work and learning more advanced techniques.

Special features:

  • Machine learning is not rocket science! An intuitive grasp of charts, graphs and the slope of a line is all you will need.
  • A number of Pitfalls sections show how to avoid common problems, such as dealing with “dirty” data and factor variables with large numbers of levels.
  • Clearing the Confusion sections resolve common misconceptions, such as dealing with unbalanced data.
  • The famous Bias-Variance Tradeoff is central to machine learning, with over 6 million Google entries, yet it is given only a passing mention in most books. Here you will see explicitly how it plays out in practice.
  • Standard R packages are used throughout, with a very simple common interface available for convenient access.

After finishing this book, you will be well equipped to start applying machine learning techniques to your own datasets, including those that arise in your job or business.

Author Bio 

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).