🔘 Book page: heather.cs.ucdavis.edu/artofml/draft.pdf
[googleapps domain=”drive” dir=”file/d/1WCUUqtEvxXsTbJ64P1wA17CEytms5_SB/preview” query=”” width=”95%” height=”480″ /]I wrote this book because: • ML is not a recipe. It is not a matter of knowing the syntax and mechanics of various software packages.• ML is an art, not a science. (Hence the title of this book). • One does not have to be a math whiz or know advanced math in orer to use ML effectively, but one does need to understand the concepts well — the Why? and How? of ML methods.
Norm Matloff’s
Summary
Most books of this sort begin with a “fluff” chapter, presenting a broad overview of the topic, defining a few terms and possibly giving the historical background, but with no technical content. Yet I know that you, the reader, want to get started right away! • We’ll present our first machine learning method, k-Nearest Neighbors (k-NN), applying it to real data. • We’ll weave in general concepts that will recur throughout the book, such as regression functions, dummy variables, overfitting, p-hacking, “dirty” data and so on. (Source: Prologue: Regression models from book).
Author
Norm Matloff’s. “Dr. Norm Matloff is a professor of computer science at the University of California at Davis, and was formerly a professor of statistics at that university. He is a former database software developer in Silicon Valley, and has been a statistical consultant for firms such as the Kaiser Permanente Health Plan. He was born and raised in the Los Angeles area, and has a PhD in pure mathematics from UCLA, specializing in probability/functional analysis and statistics”. (Source: heather.cs.ucdavis.edu/matloff.html).
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