Abstract:Summer vacation is coming. Don’t go out for a walk. Read and study

In this summer vacation, you can read these free books about machine learning and data science. They can open a window for you to see machine learning and data science. If you want to know more good free books after reading this article, please check the previous or following articles in this series.

10 free books for machine learning and data science

1. Python Data Science Handbook

By Jake vanderpras

This book introduces the core libraries necessary for data processing in Python, especially IPython, numpy, pandas, Matplotlib, scikit lean and related software packages. Before that, you need to master python. If you want to master the language quickly, you can refer to the “a whirlwind tour of Python” for a quick introduction to Python for researchers and scientists.

2. Neural Networks and Deep Learning

By Michael Nielsen

This is a free online book. Through this book, you will know that neural network is a beautiful example of biological heuristic programming, so that computers can learn from observation data. Deep learning is a powerful neural network learning technology.

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

3. Think Bayes

By Allen B. Downey

This book mainly introduces how to use calculation method to deal with 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 about it also use mathematical symbols. This book uses Python code instead of mathematics, so “integral” becomes “sum”. This is a feature of the book.

4. Machine Learning & Big Data

By karee alkaseer

The purpose 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 is lost in detail or jargon. In addition, in general, existing libraries can solve the problem at hand, but sometimes they abstract and hide the basic concepts in their own way, which is why they are called “black boxes”. The book also tries to clarify the basic concepts that are abstracted and hidden in the “black box”. It is a work in progress, its content will gradually enrich.

5. Satistical Learning with Sparsity:The Lasso and Generalizations

By Trevor Hastie, Robert Tibshirani, Martin Wainwright

In the past decade, computing and information technology have developed rapidly. With its application, a large number of data have emerged in the fields of medicine, biology, finance and marketing. Under a common conceptual framework, this book expounds some important viewpoints in these fields.

6. Statistical inference for data science

By Brian caffo

As a part of data science, this book is a supporting book of statistical inference. If you don’t take this course, you can also study this book alone with the videos on YouTube.

This book aims to introduce the important field of statistical reasoning at a low cost, so that students with programming ability can use these skills in data science or statistics.

7. Convex Optimization

By Stephen Boyd & Lieven Vandenberghe

The main content of this book is about convex optimization, which is a special kind of mathematical optimization problems, including least squares and linear programming problems. As we all know, the least square and linear programming problems have a quite complete theory, appear in various applications, and can be solved by numerical method very effectively. The basic point of this book is that the same can be said for convex optimization problems of larger classes.

8. Natural Language Processing with Python

By Steven Bird & Ewan Klein & Edward Loper

This book is based on python programming language and an open source library called nltk. “Natural language” refers to the language used for human daily communication. Different from programming languages and digital symbols, natural language has been developing from generation to generation, and it is difficult to determine with clear rules. In order to make computers understand natural language better, we develop and use natural language processing (NLP). This book is about natural language processing (NLP).

9. Automate the Boring Stuff with Python

By AI sweigart

Have you ever spent hours renaming 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 very handy. Once you have mastered the basic knowledge of programming, you can create Python programs and easily solve those tedious things.

10.Social Media Mining: An Introduction

By Reza zafarani & Mohammad Ali Abbasi & Huan Liu

The development of social media in the past decade has revolutionized the way individuals interact and businesses in the industry. Individuals interact and share through social media to generate a large amount of data.

In this book, you will learn that social media mining integrates social media, social network analysis and data mining, providing a convenient and consistent platform for students, practitioners, researchers, etc. We will also learn about the potential of social media mining.

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