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I’m nearing completion of writing my new book, *The Art of Machine Learning: Algorithms+Data+R*, to be published by the whimsically named No Starch Press. I’m making a rough, partial draft available, and welcome corrections, suggestions and comments.

I’ve been considering doing such a project for some time, intending to write a book that would on the one hand serve as “machine learning for the masses” while ardently avoiding being of a “cookbook” nature. In other words, the book has two goals:

- The math content is kept to a minimum. Readers need only be able to understand scatter plots and the like, and know the concept of the slope of a line. (For readers who wish to delve into the math, a Math Companion document will be available.)
- There is strong emphasis on building a solid intuitive understanding of the methods, empowering the reader to conduct effective, penetrating ML analysis.

As I write in the preface (“To the Reader”),

“Those dazzling ML successes you’ve heard about come only after careful, lengthy tuning and thought on the analyst’s part, requiring real insight. This book aims to develop that insight.”

The language of instruction is R, using standard CRAN packages. But as I also write,

“…this is a book on ML, not a book on using R in ML. True, R plays a major supporting role and we use prominent R packages for ML throughout the book, with code on almost every page. But in order to be able to use ML well, the reader should focus on the structure and interpretation of the ML models themselves; R is just a tool toward that end.”

So, take a look, and let me know what you think!

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