The COVID-19 pandemic has put our lives at a standstill. In these unprecedented times of known fear, we need to keep ourselves sane and read books that will embolden us for the tough times ahead.
Data science and machine learning have bestowed the humans with the power to use data and run automated tasks. It is also important to note that we’re all scouting for a change in our lifestyle, while machine learning, AI, and data science are taking over the world.
Reading more about these technologies will get the ball rolling. Below are the top charted data science and machine learning books to gain a better perspective on these technologies.
- Year of Publication: 2013
- Author: Jeffrey Stanton
- Summary: For many data science evokes images of statisticians decked up in white lab coats staring continuously at the blinking screen of a computer filled with numbers. Nothing can be farther but the truth. White lab coats are for doctors and biologists
Data science is a study that refers to the collection, preparation, visualization, analysis, management, and preservation of a humongous amount of data or information.
- Year of Publication: 2013
- Authors: Gareth James, Daniela Witten, Trevor Hastie, and Robert Tibshirani
- Summary: This book leans toward finding possible ways that provide advice on how to improve the sales of particular products, this is what statistical learning comprises of. With the help of statistics, data scientists can make multiple of informed decisions for the company.
- Year of Publication:2015-2018
- Author: Roger D. Peng and Elizabeth Matsui
- Summary: Data analysis is challenging, but the most challenging part is finding people who can explain how to do it. The truth is some people are extremely good at doing what they do except they are yet to enlighten us about their thought process. In this book, you will cover topics like data analysis as art, epicycles of analysis, setting the scene, epicycle of analysis, setting expectations, collecting information, comparing expectations to data, and applying the epicycle of the analysis process.
- Year of Publication:2017
- Authors: Carl Shan, Henry Wang, William Chen, and Max Song
- Summary: This data science handbook is a complete set of compilations of interviews with 25 renowned data scientists in which they have shared their insights, stories, and have given their best advice. Some of the renowned names like Hilary Mason and DJ Patil catapulted the data science field into national attention. This book is not a technical manual toward data science rather are ideas and insights given by the world’s best data scientists.
- Year of Publication: 2012
- Author: Roger D. Peng
- Summary: The book ‘R Programming for Data Science’ talks about how R programming has become a major factor in the data science realm. Owing to its features like power, sophistication, expressiveness, and flexibility R language has become the most popular choice by data scientists. This book will teach you the fundamentals of R programming, how to write functions, how to prepare datasets, or how to debug or optimize code.
- Year of Publication: 2014
- Author: Shai Shalev-Shwartz and Shai Ben-David
- Summary: The rise for machine learning skills is bound to grow at a rapid pace. The major purpose of this book is aimed at giving people the first hand toward learning machine learning skills and to get a hold of the algorithmic paradigm it offers. Besides this, the book also provides theoretical account of every fundamental underlying machine learning to help transform the derivations into practical algorithms.
- Year of Publication:2018
- Author: Andrew Ng
- Summary: Machine learning and AI is transforming the world as we know. The Machine Learning Yearning book focuses on building an AI strategy to have a clearer vision in guiding the team while taking up a machine learning project. Machine learning is undoubtedly the foundation for countless applications such as speech recognition, email anti-spam, product recommendation, web search, etc. According to Andrew, this book will help its readers gain better insight to develop AI systems.
- Year of Publication: 2007
- Author: David Kriesel
- Summary: If you’re looking to gain deep insights into neural networks then this book is ideal for you. It starts by talking about neural networks, the significance and further moves into defining the 100-step rule, simple application examples – the classical way and the way of learning. A brief history of neural networks can be found in this book. Before reading this book make sure you have substantial knowledge of calculus and linear algebra.
- Year of Publication: 2015
- Authors: Ian Goodfellow, YoshuaBengio and Aaron Courville
- Summary: This book is overall a comprehensive book recommended for people interested in deep learning. This book is ideal for university students, for individuals looking to begin their career in AI or deep learning, and for software engineers with zero knowledge in deep learning. Deep learning has been proven useful in multiple software disciplines which includes speech and audio processing, bioinformatics, robotics, computer vision, online advertising, video games, search engines, and NLP.
- Year of Publication: 2009
- Authors: Steven Bird, Ewan Klein, and Edward Loper
- Summary: “Natural Language Processing with Python” book introduces the world of natural language processing, a domain that supports multiple language technologies like automatic summarization to email filtering to predictive text, and so on. With extensive knowledge in this field, you will also learn how to use Python programs that majorly work with unstructured texts. Additionally, you will also gain accessibility toward popular linguistic databases such as treebanks and WordNet.