These two fields Data Science and Artificial Intelligence are gaining popularity and are in demand and everyone can't afford to spend too much money on books so here is the collection of online free e-books on Machine Learning starting from basics of statistics, proceeding to machine learning foundations and finally advanced level.
Following is the collection of E-Books on Machine Learning starting from basics of statistics, proceeding to machine learning foundations and finally advanced level:
Think Stats' is an introductory book to statistics and probability for people with a basic background in Python programming. It's based on a Python library for probability distributions (PMFs and CDFs). To make things easier for the reader, most of the exercises have short programs. The book also includes a case study using data from the National Institutes of Health. One of the stand-out features of this book is it covers the basics of Bayesian statistics as well, a very important branch for any aspiring data scientist.
Authors: Gareth James, Daniela Witten, Trevor Hastie and Robert Tibshirani
One of the most popular entries in this list, it's an introduction to data science through machine learning. This book gives clear guidance on how to implement statistical and machine learning methods for newcomers to this field. It's filled with practical real-world examples of where and how algorithms work.
For those with an inclination towards R programming, this book even has practical examples in R. In case you're not a programmer, don't let that put you off. This book is a gem.
This book gives a structured introduction to machine learning. It looks at the fundamental theories of machine learning and the mathematical derivations that transform these concepts into practical algorithms. Following that, it covers a list of ML algorithms, including (but not limited to), stochastic gradient descent, neural networks, and structured output learning.
What I like about this book are the chapters covering recommendation systems. It takes a fun and visually entertaining look at social filtering and item-based filtering methods and how to use machine learning to implement them. Other concepts like Naive Bayes and Clustering are also covered. There is a chapter on Unstructured text and how to deal with it, in case you are thinking about getting into Natural Language Processing.
Examples in Python are also available in case you want to practice.
As the era of Big Data rages on, mining data to gain actionable insights is a highly sought after skill. This book focuses on algorithms that have been previously used to solve key problems in data mining and which can be used on even the most gigantic of datasets.
If you're interested in neural networks, this book is for you. It starts off by covering the history of neural networks before deep diving into the mathematics and explanation behind different types of NNs. The author expects the reader to have a background of basic linear algebra and calculus.
Authors: Ian Goodfellow, Yoshua Bengio and Aaron Courville
This is probably one of the most comprehensive book written by distinguished people in deep learning field. Concepts like Monte Carlo Methods, Recurrent and Recursive Nets, Autoencoders and Deep Generative Models (among others) are covered in detail.
Authors: Steven Bird, Ewan Klein, and Edward Loper
Folks interested in getting into Natural Language processing should read this book. It's written in a lucid and clear manner with extremely well-presented codes in Python. Readers are given access to well-annotated datasets to analyse and deal with unstructured data, linguistic structure in text, among other NLP things.
No machine learning list is complete without mentioning Andrew Ng. According to him, this book will help the reader get up to speed with building AI systems. It will effectively teach you how to make the various decisions required with organizing a machine learning project.