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Top 25 Essential Machine Learning Books For Beginners And Experienced Professionals

Top Machine Learning books for beginners

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 with 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 BooksAuthorsImageDetailBest for
1Machine Learning For Absolute BeginnersOliver TheobaldStart learning Machine Learning with absolutely no experience and ML knowledge right from scratchAbsolute Beginners
2The Hundred-Page Machine Learning BookAndriy BurkovMachine 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
3Machine Learning for DummiesJohn Paul Mueller and Luca MassaronMaster the basic concepts and theories pertaining to Machine LearningBeginners
4The Elements of Statistical Learning: Data Mining, Inference, and Prediction
Trevor Hastie, Robert Tibshirani, and Jerome FriedmanWith 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
5Learning from Data: A Short Course


Yaser Abu Mostafa, Malik Magdon-Ismail, and Hsuan-Tien LinGet a good understanding of Mathematics and grab a first-hand exposure to Machine LearningBeginners
6Bayesian Reasoning and Machine LearningDavid BarberComprehensive and coherent, it develops everything from basic reasoning to advanced techniques within the framework of graphical models. Beginners
7Understanding Machine LearningShai Shalev-Shwartz and Shai Ben-DavidA theoretical account of machine learning fundamentals and the mathematical derivations that transform these principles into practical algorithms. Beginners
8Fundamentals of Machine Learning for Predictive Data Analytics: Algorithms, Worked Examples, and Case StudiesJohn D. Kelleher, Brian Mac Namee, and Aoife D’ArcyDives into the basics of machine learning required to do better predictive data analytics.Intermediate
9Programming Collective Intelligence: Building Smart Web 2.0 ApplicationsToby SegaranThis guide based on Python will help you implement ML to its best. Intermediate
10Machine Learning in ActionPeter HarringtonA no-nonsense introduction with examples showing common ML tasks & everyday data analysis, implementing classic algorithms like Apriori and AdaboosBeginners with Python knowledge
11Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent SystemsAurélien GéronProvides an intuitive understanding of the various concepts and tools that you need to develop smart, intelligent systems.Beginners
12 Understanding Machine LearningShai Shalev-Shwartz and Shai Ben-DavidA theoretical account of machine learning fundamentals and the mathematical derivations that transform these principles into practical algorithms. Beginners
13Introduction to Machine Learning with Python: A Guide for Data ScientistsAndreas C. Müller & Sarah GuidoCovers a machine learning workflow: data preprocessing and working with data, training algorithms, evaluating results, and implementing those algorithms into a production-level system.Intermediates
14Python Machine Learning By Example Yuxi (Hayden) LiuLearn to Exploit the power of Python to handle data extraction, manipulation, and exploration techniquesIntermediates
15Python Machine Learning: A Technical Approach to Machine Learning for BeginnersLeonard EddisonA Complete Step By Step Beginners Guide To Programming With PythonBeginners
16Machine Learning for HumansVishal Maini and Samer SabriA free-to-download, clear easy-to-read guide for machine learning beginners, accompanied by code, math, and real-world examples for contextFor All
17Machine Learning for Hackers: Case Studies and Algorithms to Get you StartedDrew Conway and John Myles White
Excellent book for Data Analysis in R with advanced R concepts for data wranglingExperienced Programmers
18Machine Learning: A Probabilistic Perspective (Adaptive Computation and Machine Learning series)Kevin P. MurphyA comprehensive introduction to machine learning that uses probabilistic models and inference as a unifying approach.Intermediates
19Pattern Recognition and Machine LearningChristopher M. BishopIt is an excellent guide to understanding and using statistical techniques in machine learning and pattern recognitionFor All
20 Natural Language Processing with PythonSteven Bird, Ewan Klein, and Edward LoperMaster NLP with the help of powerful Python codes in a clear, precise mannerExperienced Professionals
21An Introduction to Statistical LearningGareth James, Daniela Witten, Trevor Hastie, and Robert TibshiraniPresents some of the most important modeling and prediction techniques, along with relevant applications.Intermediates
22Deep Learning
(Adaptive Computation and Machine Learning series) 
Ian Goodfellow, Yoshua Bengio and Aaron CourvilleOffers mathematical and conceptual background, covering relevant concepts in linear algebra, probability theory and information theory, numerical computation, and machine learning. For All
23Machine LearningTom M. MitchellA 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
24Data Mining: Practical Machine Learning Tools and TechniquesIan H. Witten, Eibe Frank, and Mark A. HallProvides a thorough grounding in machine learning concepts, as well as practical advice on applying the tools and techniques to data mining projectsExperts
25Machine Learning with TensorFlowNishant ShuklaMatch your tasks to the right machine-learning and deep-learning approaches by visualizing algorithms with TensorBoard & understanding and using neural networksExperts

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

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

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 decade 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

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 colour 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 modelling 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, the Learning from Data: A Short Course book is a short and crisp explanation of the 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

Machine learning books for beginners!

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.

Meanwhile, 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. This IIT-M Certified Advanced Programming Professional and Data Science Course are for anyone who aspires to be a Data Science Professional. You may start learning from scratch with absolutely no programming knowledge & stand out as a Data Scientist in just 6 months.

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

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Author Bio

Archana
Archana
A traveler, and explorer, Archana is an active writer at GUVI. You can usually find her with a book/kindle binge-reading (with an affinity to the mystery/humor).

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