Find the top 22 Machine Learning Books for beginners and advanced Machine Learning Professionals here!

You get a free hour and decide to watch a movie. After a long search of half an hour, you end up switching off the TV and losing interest altogether! Does that happen to you? But not anymore! Because Netflix these days recommends the top movies and web series that are trending around you. Thanks to Machine Learning!

So, which web series is on your hit list today? Of course, you need not bookmark(as you would need to do this page) or save any titles again as they naturally come up on your recently watched list!

It is the tiniest wonder that Machine Learning facilitates. We have uncountable little wonder packs of Machine Learning, making lives easier. Whether in Virtual Assistants, self-driving cars, or Business intelligence, Machine Learning is hugely benefiting humankind.

Undoubtedly, the industry is looking for skilled Machine Learners who can add to their database of intellectual folks.

Indeed, the American worldwide employment website marked Machine Learning Engineers as one of the top jobs in the United States with regards to salary, growth of postings, and demand.

So, how are you planning to make your great beginning in the Machine Learning World?
Books are the smartest option to turn to. Here are the top 22 Machine learning books for beginners that will guide you in and out of Machine Learning and related fields.

Sr No.	Machine Learning Books	Authors	Image	Detail	Best for
1	Machine Learning For Absolute Beginners	Oliver Theobald		Start learning Machine Learning with absolutely no experience and ML knowledge right from scratch	Absolute Beginners
2	The Hundred-Page Machine Learning Book	Andriy Burkov		Machine Learning: packed in 100 pages! If you are starting with Machine Learning, then this easy-to-comprehend book is the best, to begin with.	Beginners
3	Machine Learning for Dummies	John Paul Mueller and Luca Massaron		Master the basic concepts and theories pertaining to Machine Learning	Beginners
4	The Elements of Statistical Learning: Data Mining, Inference, and Prediction	Trevor Hastie, Robert Tibshirani, and Jerome Friedman		With more emphasis on the concepts, this book throws light on ideas in a variety of fields such as medicine, biology, finance, and marketing in a common conceptual framework.	Beginners
5	Learning from Data: A Short Course	Yaser Abu Mostafa, Malik Magdon-Ismail, and Hsuan-Tien Lin		Get a good understanding of Mathematics and grab a first-hand exposure to Machine Learning	Beginners
6	Bayesian Reasoning and Machine Learning	David Barber		Comprehensive and coherent, it develops everything from basic reasoning to advanced techniques within the framework of graphical models.	Beginners
7	Understanding Machine Learning	Shai Shalev-Shwartz and Shai Ben-David		A theoretical account of machine learning fundamentals and the mathematical derivations that transform these principles into practical algorithms.	Beginners
8	Fundamentals of Machine Learning for Predictive Data Analytics: Algorithms, Worked Examples, and Case Studies	John D. Kelleher, Brian Mac Namee, and Aoife D’Arcy		Dives into the basics of machine learning required to do better predictive data analytics.	Intermediate
9	Programming Collective Intelligence: Building Smart Web 2.0 Applications	Toby Segaran		This guide based on Python will help you implement ML to its best.	Intermediate
10	Machine Learning in Action	Peter Harrington		A no-nonsense introduction with examples showing common ML tasks & everyday data analysis, implementing classic algorithms like Apriori and Adaboos	Beginners with Python knowledge
11	Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems	Aurélien Géron		Provides an intuitive understanding of the various concepts and tools that you need to develop smart, intelligent systems.	Beginners
12	Understanding Machine Learning	Shai Shalev-Shwartz and Shai Ben-David		A theoretical account of machine learning fundamentals and the mathematical derivations that transform these principles into practical algorithms.	Beginners
13	Introduction to Machine Learning with Python: A Guide for Data Scientists	Andreas C. Müller & Sarah Guido		Covers a machine learning workflow: data preprocessing and working with data, training algorithms, evaluating results, and implementing those algorithms into a production-level system.	Intermediates
14	Python Machine Learning By Example	Yuxi (Hayden) Liu		Learn to Exploit the power of Python to handle data extraction, manipulation, and exploration techniques	Intermediates
15	Python Machine Learning: A Technical Approach to Machine Learning for Beginners	Leonard Eddison		A Complete Step By Step Beginners Guide To Programming With Python	Beginners
16	Machine Learning for Humans	Vishal Maini and Samer Sabri		A free-to-download, clear easy-to-read guide for machine learning beginners, accompanied by code, math, and real-world examples for context	For All
17	Machine Learning for Hackers: Case Studies and Algorithms to Get You Started	Drew Conway and John Myles White		Excellent book for Data Analysis in R with advanced R concepts for data wrangling	Experienced Programmers
18	Machine Learning: A Probabilistic Perspective (Adaptive Computation and Machine Learning series)	Kevin P. Murphy		A comprehensive introduction to machine learning that uses probabilistic models and inference as a unifying approach.	Intermediates
19	Pattern Recognition and Machine Learning	Christopher M. Bishop		It is an excellent guide to understanding and using statistical techniques in machine learning and pattern recognition	For All
20	Natural Language Processing with Python	Steven Bird, Ewan Klein, and Edward Loper		Master NLP with the help of powerful Python codes in a clear, precise manner	Experienced Professionals
21	An Introduction to Statistical Learning	Gareth James, Daniela Witten, Trevor Hastie, and Robert Tibshirani		Presents some of the most important modelling and prediction techniques, along with relevant applications.	Intermediates
22	Deep Learning (Adaptive Computation and Machine Learning series)	Ian Goodfellow, Yoshua Bengio and Aaron Courville		Offers mathematical and conceptual background, covering relevant concepts in linear algebra, probability theory and information theory, numerical computation, and machine learning.	For All
23	Machine Learning	Tom M. Mitchell		A comprehensive guide on machine learning theorems with pseudocode summaries of the respective algorithms with a number of examples and case studies.	For Beginner to Experienced Machine Learning
24	Data Mining: Practical Machine Learning Tools and Techniques	Ian H. Witten, Eibe Frank, and Mark A. Hall		Provides a thorough grounding in machine learning concepts, as well as practical advice on applying the tools and techniques to data mining projects	Experts
25	Machine Learning with TensorFlow	Nishant Shukla		Match your tasks to the right machine-learning and deep-learning approaches by visualizing algorithms with TensorBoard & understanding and using neural networks	Experts

Let’s dig deep and find out the details of the top 5 Machine Learning books for beginners in this list:

But before we get started with that, here are some of our personal research resources for you: Check it out!

Get started with Machine Learning Algorithms

How to set up a Python environment for Machine Learning

Essential prerequisites for Machine Learning

Make sure you understand machine learning fundamentals like Python, SQL, deep learning, data cleaning, and cloud services before we explore them in the next section. You should consider joining GUVI’s Machine Learning Career Program, which covers tools like Pyspark API, Natural Language Processing, and many more and helps you get hands-on experience by building real-time projects.

Also, if you want to explore more about Artificial Intelligence and Machine Learning altogether through a Self-paced AI bundle course, try GUVI’s Artificial Intelligence and Machine Learning Self-Paced course.

1. Machine Learning For Absolute Beginners

A Plain English Introduction (Third Edition): 1 (Machine Learning with Python for Beginners)

Author: Oliver Theobald

About the Book:

A Newbie to Machine Learning? This book can be your best hideout. You can kickstart your Machine Learning journey here with no coding or mathematical background.  

Machine Learning concepts are very well defined and made sense in layman’s words with clear explanations, visual examples & various ML algorithms.

This book is designed for readers taking their first steps in machine learning. However, further learning will be required beyond this book to master machine learning.

Topics covered:

– Artificial Intelligence
– Big Data
– Downloading Free Datasets
– Regression
– Support Vector Machine Algorithms
– Deep Learning/Neural Networks
– Data Reduction
– Clustering
– Association Analysis
– Decision Trees
– Recommenders
– Machine Learning Careers

Are you looking for a great Machine Learning Course? Then your search ends here: Get more ideas on Machine Learning books for beginners here:

2. The Hundred-Page Machine Learning Book

Author: Andriy Burkov

About the Book:

To the point and short & sweet, this book can be read in a week! In this book learn everything that modern Machine Learning has to offer! The Hundred-Page Machine Learning Book appears to be a summary of decades years of experience of the authors of this book.

The best part of this book is the Companion Wiki, which continuously updates & flares some book chapters with auxiliary information: Q&A, code snippets, further reading, tools, and other relevant resources.

Topics covered:

• Anatomy of a learning algorithm
• Fundamental algorithms
• Neural networks and deep learning
• Other forms of learning
• Supervised learning and unsupervised learning

After reading the top Machine learning books for beginners you may check this: Top 5 Luxury Jobs in Machine Learning 

3. Machine Learning for Dummies

Authors: John Paul Mueller and Luca Massaron

About the book:

Do you wish you could get familiar with the basic concepts and theories of Machine Learning? Then you would find no better book than Machine Learning for Dummies. The topics covered in the book go with the name of the book.

Additionally, the book focuses on the practical, real-world applications of machine learning. The book mentions how to train machines to find patterns and analyze results using Python and R code with an augmented advantage of illustrations on how ML facilitates email filters, fraud detection, internet ads, web searches, etc.

Topics covered:

• Data preparation
• Machine learning techniques
• Supervised and unsupervised learning
• The machine learning cycle
• Training machine learning systems
• Tying machine learning methods to outcomes

Next on the list, 4th of the top Machine learning books for beginners:

4. The Elements of Statistical Learning: Data Mining, Inference, and Prediction

Author:

Trevor Hastie, Robert Tibshirani, and Jerome Friedman

About the book:

Though a rigorous and mathematically dense book, The Elements of Statistical Learning: Data Mining, Inference, and Prediction has a special emphasis on concepts rather than mathematics. With the liberal use of color graphics, the book gives tons of examples to highlight the concepts. 

All three authors of the book are well-known Statistics Professors at Stanford University. Trevor Hastie, Robert Tibshirani, and Jerome Friedman are prominent researchers in this area: Hastie and Tibshirani developed generalized additive models and wrote a popular book of that title.

In addition to having invented principal curves and surfaces, Author Hastie also wrote much of the statistical modeling software in S-PLUS. Author Tibshirani, on the other hand, proposed the Lasso and is co-author of the very successful book: An Introduction to the Bootstrap. Author Friedman is the co-inventor of many data-mining tools including CART, MARS, and projection pursuit.

Little wonder then that this book appears in the top 5 of our top Machine Learning books recommended for good learning.

Includes Topics:

• Ensemble learning
• High-dimensional problems
• Linear methods for classification and regression
• Model inference and averaging
• Neural networks
• Random forests
• Supervised and unsupervised learning

5. Learning from Data: A Short Course

Author:

Yaser Abu Mostafa, Malik Magdon-Ismail, and Hsuan-Tien Lin

About the book:

Included in our list of top 5 Machine learning books for beginners, Learning from Data: A Short Course book is a short and crisp explanation of Machine Learning concepts. This book can be a perfect choice for anyone who wants to get a comprehensive introduction to machine learning in less time.

It is a short course, not a hurried course. Comprehend the complex machine learning concepts with the easy explanations in this book.

Topics Covered:

• Error and Noise
• Kernel methods
• Overfitting
• Radial basis functions
• Regularization
• Support vector machines
• Validation

Kickstart your Machine Learning journey by enrolling in GUVI’s Machine Learning Career Program where you will master technologies like matplotlib, pandas, SQL, NLP, and deep learning,  and build interesting real-life UI/UX projects.

Also, if you want to explore more about Artificial Intelligence and Machine Learning altogether through a Self-paced AI bundle course, try GUVI’s Artificial Intelligence and Machine Learning Self-Paced course.

Conclusion

Yes, let’s call it a wrap for now. The discussion on books especially on Top Machine Learning Book is never-ending. There are uncountable books coming up every year. So, we will keep updating the list here. For now, these are the books you can refer to.

eanwhile, you master Machine Learning from the various Machine Learning Books for Beginners that we suggested, here’s a course in Data Science that you can take up or recommend to anyone who is interested.

Post your suggestions and queries about the top Machine Learning books for beginners in the comment section below.