Top 10 Books for Machine Learning You Should Read
Books are beautiful, words are their arsenal. Every word pushes you to imagine more, and with that, you learn more. Your own pace and your own convenience, study as you need. Wondering which is the best book for what is Machine Learning? Well, there isn’t one book for everything. That is why we have dug about and found the best books for Machine Learning ranging from complete beginners to advanced developers.
This article has been broken down into the following segments:
- What is Machine Learning?
- Beginner Books for Machine Learning
- Machine Learning For Absolute Beginners
- Machine Learning For Dummies
- Artificial Intelligence: A Modern Approach
- Machine Learning in Action
- Machine Learning for Hackers
- Advanced Books for Machine Learning
- Python Machine Learning
- Data Science from Scratch
- Programming Collective Intelligence
- Make Your Own Neural Network
- Pattern Recognition and Machine Learning
What is Machine Learning?
Machine Learning is the process of creating models that can perform a certain task without the need for a human explicitly programming it to do something.
Machine Learning is, in simple terms, teaching your computer about something. It could be to differentiate between a dog and a cat or differentiate between fruits, diagnose cancer in patients, create a chatbot that helps someone in depression. It could be to teach your computer to read, all this is made possible through Machine Learning. With that out of the way, let’s find out all the best books available to learn Machine Learning!
Beginner Books for Machine Learning
Let us begin by firstly going through some of the beginner books as that makes the most sense. So please go through the recommendations that we have for all those new to Machine Learning.
Machine Learning For Absolute Beginners by Oliver Theobald
The title of the book says it all. Machine Learning for Absolute Beginners is for anybody who is entirely new to it. You may not have any programming knowledge or mathematical knowledge and you can still start out with Machine Learning using this book. It is just that good. The language of the author and how he has explained everything keeping in mind the perspective of someone who is new to all of this is just one of the best in the market today.
It has pretty visuals and graphs with a really good explanation about every algorithm and some coding in Python to put Machine Learning in the practical sense. So all of you who are new, this is the book to get started with.
Machine Learning For Dummies by John Paul Mueller and Luca Massaron
Moving up the level a bit, we have Machine Learning for Dummies which looks into the theory and basic concepts of Machine Learning to make the readers get used to all the jargons of it. It teaches you how to apply Machine Learning in practicality and introduces the programming languages and tools that are required to apply them efficiently.
It introduces coding with Python and R programming language and how they can be used to teach your computer about certain patterns and analyze results. You can learn how applications of Machine Learning are used in the real world and is a great starter into the world of Machine Learning.
Artificial Intelligence: A Modern Approach by Stuart Russell and Peter Norvig
For all those wondering what has Artificial Intelligence got to do with Machine Learning? Machine Learning is a subfield of Artificial Intelligence and they share a lot in common. This book is a perfect move up from the previous two as it covers both the topics in good detail and the language is really simple to understand.
It talks about the differences between them and how you need to understand the problem perfectly and move accordingly to find the solution for it. A really good book to help you differentiate between problem approaches and find the needed path.
Machine Learning in Action by Peter Harrington
Moving ahead into the programming realm, we have this beautiful book by Peter who has designed it very efficiently and has made it user-friendly. He introduces all the techniques that are required to get started with building machine learning algorithms and how to obtain data from these algorithms for Data Analysis.
It is helpful if you are familiar with coding preferably in Python so that you do not fall short of understanding anything. This is probably the best tutorial for beginners to get started with coding for Machine Learning.
Machine Learning for Hackers by Drew Conway and John Myles White
Now for all those of you who are really good at coding but have a bad background in mathematics, this is the book for you guys to go with. Do not think of hackers as someone related to Cyber-Security but hacker here refers to those who are already good at coding. This book stresses deeply on the math that is required for Machine Learning and uses real-world scenarios and use-cases which can help you get a hang of it. Typical Machine Learning problems with R programming language are the start and move to advanced topics where you will be taught how to build a recommendation system and those sorts of applications. It is the book to study if you are already comfortable with advanced coding.
Now that we have covered the books for beginner levels, let’s move up the level and take a look at the books for Advanced Concepts and more.Advanced Books for Machine Learning
Advanced Books for Machine Learning
Python Machine Learning by Sebastian Raschka and Vahid Mirjalili
This book is probably the only one that focuses on one programming language only which is Python and it helps you understand and develop various Machine Learning, Deep Learning, and Data Analysis algorithms. It goes over various powerful libraries such as the Scikit-Learn for implementing various Machine Learning algorithms. Following that, it also teaches you about Deep Learning using the Tensor Flow module. It also teaches you the various methods which can be used to improve the efficiency of the model you make and lastly shows you the various data analysis opportunities that you can achieve using Machine and Deep Learning.
Data Science from Scratch with Python by Joel Grus
Once you are done with Python Machine Learning, go ahead and start off with this book as it teaches you what exactly is Data Science and all the jargons it has. As Machine Learning basics have been covered, this will help you understand further what exactly can you do with the data that you acquire and much more. Yes, you do not need to know Machine Learning prior but having understood it brings better depth and understanding of the subject.
Programming Collective Intelligence by Toby Segaran
What do you do with Machine Learning? Where do you apply it practically? This book here has the answers to it all. It is a really fascinating book that teaches you how to apply Machine Learning to develop smarter applications. It teaches you how to apply Machine Learning for websites, applications and more. This book has the project-based approach where it teaches you a project, how it has been made and more, then adding the flavours of Machine Learning and significantly improving the efficiency of the project. This is probably the best way to do that as it teaches you the importance of Machine Learning.
Make Your Own Neural Network by Tariq Rashid
Machine Learning fails when the data grows. And so, Deep Learning comes to the play. This book is beautiful for everyone who wants to study about Deep Learning and how they are better than typical Machine Learning. It teaches you how to build your neural networks in Python with practical examples and problems. The writing is beautiful and helps you understand this rather difficult subject.
Pattern Recognition and Machine Learning by Christopher M. Bishop
For everyone aiming to be Data Scientists, this is the book you need. It covers various ever-advancing topics of statistics and probability and also goes through finding what patterns make data better or worse and how to work with them for Machine Learning. From general examples to real-world data gathering and pattern study, it teaches all of it to you. It is definitely the book only advanced programmers should go ahead with. It will definitely help you better yourself and probably land you a good job in Machine Learning.
That basically wraps up our recommendations to you, ranging from the beginners all the way up to the most advanced fields. We hope you like our recommendations.
If you wish to check out more articles on the market’s most trending technologies like Python, DevOps, Ethical Hacking, then you can refer to Edureka’s official site.
Do look out for other articles in this series which will explain the various other aspects of Data Science.
2.Math And Statistics For Data Science
9.Introduction To Machine Learning
12.How To Create A Perfect Decision Tree?
13.Top 10 Myths Regarding Data Scientists Roles
15.Data Analyst vs Data Engineer vs Data Scientist
16.Types Of Artificial Intelligence
17.R vs Python
18.Artificial Intelligence vs Machine Learning vs Deep Learning
20.Data Analyst Interview Questions And Answers
21.Data Science And Machine Learning Tools For Non-Programmers
22.Top 10 Machine Learning Frameworks
23.Statistics for Machine Learning
25.Breadth-First Search Algorithm
26.Linear Discriminant Analysis in R
27.Prerequisites for Machine Learning
28.Interactive WebApps using R Shiny
Originally published at https://www.edureka.co on November 5, 2019.
