Statistical Methods for Machine Learning: Discover How to Transform Data into Knowledge with Python

by Jason Brownlee

Statistics is a pillar of machine learning. You cannot develop a deep understanding and application of machine learning without it. Cut through the equations, Greek letters, and confusion, and discover the topics in statistics that you need to know.

Using clear explanations, standard Python libraries, and step-by-step tutorial lessons, you will discover the importance of statistical methods to machine learning, summary stats, hypothesis testing, nonparametric stats, resampling methods, and much more.

Genres: Mathematics

291 pages, ebook

Published January 1, 2018

About the author

Jason Brownlee, Ph.D. trained and worked as a research scientist and software engineer for many years (e.g. enterprise, R&D, and scientific computing), and is known online for his work on Computational Intelligence (e.g. Clever Algorithms), Machine Learning and Deep Learning (e.g. Machine Learning Mastery, sold in 2021) and Python Concurrency (e.g. Super Fast Python).

Jason writes fiction under the pseudonym J.D. Brownlee: https://www.goodreads.com/jdbrownlee

Reviews

[Arun · Rating 4 out of 5]

A good hands-on overview of statistical methods for ML using Python. Each topic/section only covers enough basics to help one to explore the topic further in detail (there is a summary section at the end of each chapter that links to books/articles). It is not a beginner book though, you should already have some basic understanding of statistical terminologies like critical value, significance test, p-value etc. Best part of the book is it is hands-on where each algorithm, concepts are introduced and then implemented in straightforward python (using industrial strength libraries like scipy, numpy, statsmodel, matplotlib etc).

PS: A good beginner book on statistics is Statistics Unplugged by Sally Caldwell (https://www.goodreads.com/book/show/9...)

[Shangyun Lu · Rating 4 out of 5]

A good material to go through the must-know concepts and theories used in the work, especially the first 3 quarters of the book. The author dives into various statistical tests in the last chapters and I basically just skimmed through as I rarely use them.