Readings

A Course in Machine Learning (ebook) by Hal Daumé III – Another complete introduction to machine learning topics. Each chapter is individually downloadable. 189 Pages.

A Programmer’s Guide to Data Mining (ebook) by Ron Zacharski – This one is an online book, each chapter downloadable as a PDF. It’s also still in progress, with chapters being added a few times each year.

An Introduction to Data Science (ebook) by Jeffrey Stanton – Overview of the skills required to succeed in data science, with a focus on the tools available within R. It has sections on interacting with the Twitter API from within R, text mining, plotting, regression as well as more complicated data mining techniques. 195 Pages.

An Introduction to Statistical Learning with Applications in R (ebook) by James, Witten, Hastie & Tibshirani – This book is fantastic and has helped me quite a bit. It provides an overview of several methods, along with the R code for how to complete them.

Bayesian Reasoning and Machine Learning (ebook) by David Barber – This is an undergraduate textbook. It includes an overview, derivations, sample problems and MATLAB code. 648 Pages.

Crystallizing Public Opinion (ebook) by Edward Bernays – This is a foundational book in the area of public relations and propaganda.

Data Mining and Analysis, Fundamental Concepts and Algorithms (ebook) by Zaki & Meira – This title is a text book that looks to be a complete introduction with derivations & plenty of sample problems. 599 Pages.

Decision Analysis for the Professional (ebook)

Elements of Statistical Learning (ebook)

Handbook of Applied Cryptography

Information Theory, Inference and Learning Algorithms (ebook) by David J.C. MacKay – Nice overview of machine learning topics, including an introduction and derivations. One nice feature of this book is that it has a chart that shows how various topics are related to one another. 628 Pages.

Internet Modern History Sourcebook (ebook)

Machine Learning – The Complete Guide (ebook) This one is a collection of Wikipedia articles organized into chapters & downloadable in a number of formats. Because its a collection of individual articles, it covers quite a bit more material than a single author could write. This is an incredible resource.

Probabilistic Programming & Bayesian Methods for Hackers (ebook) by Cam Davidson-Pilson – This book is absolutely fantastic. The author explains Bayesian statistics, provides several diverse examples of how to apply and includes Python code. Each chapter is an iPython notebook that can be downloaded.

The Elements of Statistical Learning (ebook) by Hastie, Tibshirani & Friedman – This is an in-depth overview of methods, complete with theory, derivations & code. I’d definitely consider this a graduate level text. I’d also consider it one of the best books available on the topic of data mining. 745 Pages.

Think Bayes, Bayesian Statistics Made Simple (ebook) by Allen B. Downey – Another great, easy to digest introduction to Bayesian statistics. The author’s premise is that Bayesian statistics is easier to learn & apply within the context of reusable code samples. It includes a number of examples complete with Python code. 195 Page

 

 

 

 

 

 

 

 

 

 

 

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