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Free Data Science eBooks - January 2017

Our regular newsletter subscribers know that every month we scour the internet seeking out free eBooks to help you on your educational journey. Well, it has been so popular that rather than just send the links in the newsletters we decided to create a resources section here on the website.

I hope this will prove to be a valuable resource to you that you will visit regularly (and invite your friends too).

If you haven't subscribed to our newsletter yet, why not subscribe using the form on the right - you'll be the very first to know when new resources are published.

 

Disclosure: as well as links to the free ebooks we may also include links to non-free versions of the same books, and we may earn an affiliate commission for purchases you make when using those links

You can find further details in our TCs

 

3 Free Data Science Books for January

 

This month, we have 3 Data Science eBooks for you. They're all FREE, so get stuck in.

 

Enjoy!

 


 

Probabilistic Programming and Bayesian Methods for Hackers

by Cam Davidson-Pilon

The Bayesian method is the natural approach to inference, yet it is hidden from readers behind chapters of slow, mathematical analysis. The typical text on Bayesian inference involves two to three chapters on probability theory, then enters what Bayesian inference is. Unfortunately, due to mathematical intractability of most Bayesian models, the reader is only shown simple, artificial examples.

This can leave the user with a ‘so-what’ feeling about Bayesian inference. In fact, this was the author’s own prior opinion.

"After some recent success of Bayesian methods in machine-learning competitions, I decided to investigate the subject again. Even with my mathematical background, it took me three straight days of reading examples and trying to put the pieces together to understand the methods. There was simply not enough literature bridging theory to practice. The problem with my misunderstanding was the disconnect between Bayesian mathematics and probabilistic programming."

"That being said, I suffered then so the reader would not have to now. This book attempts to bridge the gap."


NOTE: This ebook is offered for free in only the pdf version. If you would rather have an alternative version (including paperback) you can get a paid copy from Amazon ← *big fat scary affiliate link!*

 

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Data Mining And Analysis: Fundamental Concepts and Algorithms

by Mohammed J. Zaki and Wagner Meira, Jr.

The fundamental algorithms in data mining and analysis are the basis for business intelligence and analytics, as well as automated methods to analyze patterns and models for all kinds of data. This textbook for senior undergraduate and graduate data mining courses provides a comprehensive overview from an algorithmic perspective, integrating concepts from machine learning and statistics, with plenty of examples and exercises.

"This book by Mohammed Zaki and Wagner Meira Jr is a great option for teaching a course in data mining or data science. It covers both fundamental and advanced data mining topics, explains the mathematical foundations and the algorithms of data science, includes exercises for each chapter, and provides data, slides and other supplementary material on the companion website."

Gregory Piatetsky-Shapiro, Founder, ACM SIGKDD, the leading professional organization for Knowledge Discovery and Data Mining.


NOTE: This ebook is offered for free in only the pdf version. If you would rather have an alternative version (including paperback) you can get a paid copy from Amazon ← *big fat scary affiliate link!*

 

 

Building Machine Learning Systems with Python

by Willi Richert and Luis Pedro Coehlo

As the Big Data explosion continues at an almost incomprehensible rate, being able to understand and process it becomes even more challenging. With Building Machine Learning Systems with Python, you'll learn everything you need to tackle the modern data deluge – by harnessing the unique capabilities of Python and its extensive range of numerical and scientific libraries, you will be able to create complex algorithms that can ‘learn’ from data, allowing you to uncover patterns, make predictions, and gain a more in-depth understanding of your data.

Featuring a wealth of real-world examples, this book provides gives you with an accessible route into Python machine learning. Learn the Iris dataset, find out how to build complex classifiers, and get to grips with clustering through practical examples that deliver complex ideas with clarity. Dig deeper into machine learning, and discover guidance on classification and regression, with practical machine learning projects outlining effective strategies for sentiment analysis and basket analysis. The book also takes you through the latest in computer vision, demonstrating how image processing can be used for pattern recognition, as well as showing you how to get a clearer picture of your data and trends by using dimensionality reduction.

Keep up to speed with one of the most exciting trends to emerge from the world of data science and dig deeper into your data with Python with this unique data science tutorial.

NOTE: This ebook is offered for free in only the pdf version. If you would rather have an alternative version (including paperback) *with a fresh new cover design* you can get a paid copy from Amazon ← *big fat scary affiliate link!*

 


 

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