There are people who love reading books but can't buy them as it is not affordable for them. And the books which are related to machine learning and data science are not even cost-effective. This would be quite fair as writing the books take a lot of effort and hard work in publishing that particular piece.
But there are many writers who have made these books available for the students as well other people. That is for free! For becoming a data scientist and an engineer of artificial intelligence, you have the choice of asking more.
Here you will get a collection of books which is on machine learning. And this list begins with the statistics and then comes the foundation of machine learning and then comes the machine learning which is advanced.
If you are willing to access these books you have the link which is provided below.
Think stats is a book which is introductory to statistics and the probability for the ones who are having basic knowledge in the Python programming language. Its based on a Python library for probability distributions (PMFs and CDFs). For the readers, things are made very easy, as short programs are provided in many of the exercises. The case study is also there in this book which also includes a case study using data from the National Institutes of Health.
Speaking of Bayesian statistics, this one is a classic. This takes a Bayesian statistics approach to machine learning. The ones who are willing to go in the field of machine learning should read this book and grab good knowledge about machine learning.
Authors: Gareth James, Daniela Witten, Trevor Hastie, and Robert Tibshirani
This is the most leading book in the list, which is an introduction to the data science through machine learning. Implementing the methods of machine learning and statistics is given in this book with clear guidance for the beginners in this field. Working of the algorithms is there in this book, and has real-world examples which are practical.
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.
Here you will get the structured intro of machine learning. It also has many theories which are fundamental and also have mathematical derivations which alter these concepts into the algorithms which are practical. Following that, it covers a list of ML algorithms, including (but not limited to), stochastic gradient descent, neural networks, and structured output learning.
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 books 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.