10 free books to Read in machine learning and data science

This summer, if you are interested, read these free books on machine learning and data science. They can give you a window into machine learning and data science. If you want more free good books after reading this article, check out the previous article in this series or the ones below.

1. Python Data Science Handbook

By Jake VanderPlas

This book introduces the core libraries necessary to process data in Python, especially IPython, NumPy, Pandas, Matplotlib, SciKit-lean, and related packages. You’ll need to know Python before you do, and if you want to get up to speed on the language, see “A Whirlwind Tour of Python,” A quick start on Python for researchers and scientists.

2. Neural Networks and Deep Learning

By Michael Nielsen

This is a free online book. In this book you will learn that neural networks are a beautiful example of biological heuristic programming that allows computers to learn from observational data. Deep learning is a set of powerful neural network learning techniques.

At present, neural networks and deep learning provide many effective solutions to problems in image recognition, speech recognition and natural language processing (NLP). Through this book you will learn more about the core concepts behind neural networks and deep learning.

3. Think Bayes

By Allen B.Downey

This book mainly introduces how to use computational methods to handle Bayesian statistics.

If you want to use the skills in this book to learn other skills, you need to know how to program.

Bayesian statistics is based on mathematical concepts such as calculus, and most books on it use mathematical notation. This book uses Python code instead of math, so “integral” becomes “sum”. This is a feature of the book.

4. Machine Learning & Big Data

By Karee Alkaseer

The goal behind this book is to make it easy for software engineers to use machine learning models without relying on libraries. In most cases, the concept behind the model or technology is simple and intuitive, but gets lost in the details or jargon. In addition, existing libraries generally solve the problem at hand, but sometimes they go out of their way to abstract and hide basic concepts, which is why they are called “black boxes.” The book also attempts to clarify the basic concepts abstracted and hidden in the “black box”. It’s a work in progress, and it’s slowly getting richer.

5. Satistical Learning with Sparsity:The Lasso and Generalizations

By Trevor Hastie,Robert Tibshirani and Martin Wainwright

Computing and information technology have developed rapidly in the past decade. As it is applied, a wealth of data is emerging in fields such as medicine, biology, finance and marketing. This book illustrates some of the important ideas in these areas under a common conceptual framework.

6. Statistical inference for data science

By Brian Caffo

As part of the major in data science, this book is a companion book to the Statistical Inference course. If you’re not taking the course, you can also study the book on your own with a YouTube video about it.

This book aims to provide a low-cost introduction to the important field of statistical reasoning, enabling students with programming skills to apply those skills to data science or statistics.

7. Convex Optimization

By Stephen Boyd & Lieven Vandenberghe

The main content of this book is about convex optimization, a special class of mathematical optimization problems that include least squares and linear programming problems. It is well known that the least squares and linear programming problems have a fairly complete theory that appears in a variety of applications and can be solved numerically very efficiently. The basic idea of this book is that the same can be said for larger classes of convex optimization problems.

8. Natural Language Processing with Python

By Steven Bird & Ewan Klein & Edward Loper

The book is based on the Python programming language and an open source library called the Natural Language Toolkit (NLTK). “Natural language” refers to the language used for everyday human communication. Unlike languages such as programming languages and numerical symbols, natural languages evolve over generations and are difficult to define with clear rules. In order for computers to better understand natural language, we have developed natural language processing (NLP). This book is about natural language processing (NLP).

9. Automate the Boring Stuff with Python

By AI Sweigart

Do you ever get annoyed when you spend hours renaming files or updating hundreds of cells in a table? In this book, you will learn how to use Python to solve these problems easily. Python is pretty easy to get started with, and once you’ve mastered the basics of programming, you can create Python programs and do the tedious work with ease.

10.Social Media Mining: An Introduction

Reza Zafarani & Mohammad Ali Abbasi & Huan Liu

The growth of social media over the past decade has revolutionized the way individuals interact and industries conduct business. Individuals interact and share vast amounts of data through social media.

In this book, you will learn about Social Media Mining, which integrates Social Media, Social network analysis and data Mining to provide a convenient and consistent platform for students, practitioners, researchers and more. We will also learn about the potential of Social Media Mining.