Best Machine Learning Books for Beginners and Advanced Programmers

best machine learning books
Written by Shusree Mukherjee

Are you a data analyst, or are you into artificial intelligence? Whatever field you are interested in, these are all within the field of machine learning. All the automated tasks are being made possible by machine learning. If you want to know more about machine learning, then here are 11 books that can help you understand the subject better. These are the best machine learning books that include both free and paid options.

Best machine learning books

Here are the best machine learning books to learn from:


Programming Collective Intelligence: Building Smart Web 2.0 Applications


Programming Collective Intelligence: Building Smart Web 2.0 Applications was written by Toby Segaran and was first published by O Reilly media in the year 2007. If you worried that this book might not be up to date because it was written so long back, then you laid your worries to rest. The writer has introduced machine learning with the programming language python. If you want to know more about python and how to implement machine learning, then this is the book for you.


While the introduction to machine learning is short, the book goes into detail about creating machine learning algorithms and then implement them properly. It mainly focuses on developing programs that can be used to read and analyze data from various websites. 


The book is perfect for anyone who wants a step by step guide to developing machine learning programs and is not recommended for those who need to understand the concept of machine learning, before proceeding to implementation. Some of the topics that the writer covers in this book are non-negative matrix factorization, bayesian filtering. It also contains how to detect groups and patterns, how to make predictions, search engine algorithms, collaborative filtering techniques, and support vector machines.


The book is available in paperback as well as in digital format. You can buy the book from the Kindle Store. 


Not Beginner-Friendly


The Elements Of Statistical Learning: Data Mining, Inference, And Prediction


The second edition of this book has just been released. Robert Tibshirani, Trevor Hastie wrote this book, and Jerome Friedman mainly focuses on stats and its link to machine learning. The book was published by Springer and is available in stores in hardback and on Kindle store for those that prefer a digital book. 


As already mentioned, the book focuses entirely on statistical data, and it explains the machine learning algorithms and programs in maths. This book is meant for those that are interested in stats and machine learning and have advanced knowledge in linear stats. If your knowledge in maths and stats, in particular, is not advanced, you will have difficulty understanding the ideas mentioned in the book. The covered topics are supervised and unsupervised learning, neural networks, high-dimensional problems, ensemble learning, random forests, linear methods for classification and regression, model inference, and averaging. 


Not Beginner-Friendly


Learning From Data: A Short Course


If you tend to have lower concentration and need shorter explanations instead of beating around the bush, this is the right book for you. The book is perfect for beginners who want to quickly understand the concept of machine learning before they can understand the more advanced and complex concepts in machine learning. The fact that this book does not complicate things, and it makes it more accessible to beginners. 


Only the first edition of the book has been released by the publisher AMLBook. 3 authors have co-written the book named Malik Magdon-Ismail, Yaser Abu Mostafa, and Hsuan-Tien Lin. You can also watch online videos of Yaser Abu Mustafa, explaining machine learning. The book is available in both hardcover and Kindle versions. Some of the topics that you will find in the book are–kernel methods, support vector machines, regularisation, error and noise, radial basis functions, validation, and overfitting. 




Bayesian Reasoning And Machine Learning


This book about machine learning is written by a Cambridge University professor, David Barber, and published by the Cambridge University press is perfect for college graduates as well as undergraduate students who are currently completing their course in components science. The book comes recommended as the must-have book for all computer geeks who want to pursue machine learning in the future. 


The book attained it’s must-have status because it introduces complex concepts in simple words and provides plenty of examples to help the reader understand it. The ideas presented in the book can be understood even by those that do not come from a linear algebra or calculus background. 

The book does not just provide explanations, but also contain exercises to help the students practice. The online resources and references will help those who wish to dive deeper into the field. The software package that comes with the book will allow students to run the program and learn it from scratch. Students can expect to learn about these concepts in the book naive Bayes algorithm, probabilistic reasoning, dynamic models, learning probabilistic models, approximate interference, and the framework of graphic models. The book is available online and in both paperback and hardcover format.


Beginner Friendly


Machine Learning For Dummies


As you can already tell from the title of the book, the book is meant for beginners. If you have never been exposed to machine learning, but you want to have a basic understanding of it, then you should buy this book. For Dummies have published this book written by John Paul Mueller and Luca Massaron. 


The book focuses on two programming languages, python and R Code, and then explains how to write machine learning programs in python and R Code. The book will first give you a detailed introduction to the basic concepts in machine learning and familiarise you with the real-time implementation of the machine learning programs. 


You can expect to learn these points machine learning techniques, tying machine learning methods or outcome, supervised and unsupervised learning. Also, it contains data preparation, machine learning cycle, email filters, fraud detection, web searches, and internet ads with machine learning algorithms. The book is available as a digital book, as well as in paperback format. 


Beginner Friendly


Understanding Machine Learning: From Theory To Algorithms


This is a free book that is co-written by Shai Ben-David and Shai Shalev-Shwartz. The book familiarises the readers with the fundamentals of machine learning and introduces complex theories and concepts to the readers. This is one of the best free machine learning books that are currently available, as it tends to shed light on some concepts that are usually not covered in other books. 


Not Beginner-Friendly


Data Mining: Practical Machine Learning Tools And Techniques


More and more people are getting interested in data mining, and are considering it for their career. If you want to learn about data mining and the techniques used in the field along with a basic understanding of machine learning, then this book will help you a great deal. This is the fourth edition of the book that is published by the Morgan Kaufmann publisher and is written by Ian H. Witten, Mark A. Hall, and Eibe Frank. The book is available in paperback and Kindle format. 


The book is ideal for those that want to learn about the technical aspects of data mining and machine learning. You get to learn about methods that are used to obtain data, different input, and output techniques to get varied results, clustering, instance-based learning, predicting performance. Also, comparing data mining methods, linear models, traditional and modern data mining methods, knowledge representation and clusters, and statistical modeling. 


Not Beginner-Friendly


Machine Learning


The book is penned by Tom M. Mitchell and published by McGraw Hill Education. Only the first edition of the book has been released so far, and it is only available in paperback format. You cannot find it in the Kindle Store. The book focuses mainly on theories and provides a summary of various algorithms. Since it goes right into methods instead of introducing the basic concepts, the book is not suitable for beginners. The book is full of case studies and various examples that are meant to be helpful for readers to grasp the concept quickly. 


The book also contains various homework and exercises that will help you practice it. You will learn the following things– inductive logo programming, reinforcement learning, genetic algorithms, introduction to primary approaches to machine learning, machine learning concepts, and techniques. 


Not Beginner-Friendly


Machine Learning In Action


The book about machine learning is penned by Peter Harrington and is published by Manning publishers. The book aims to educate readers on the basic and advanced concepts of machine learning in simple, straightforward words. Since the book does not make things too complicated instead of keeping it easy, it suits both beginners and professionals. 


The majority of the machine learning programs that are mentioned in the book as examples are in python language, so you need to be a little familiar with the programming language. 


The book explains the algorithms, and you can also use the book to develop machine learning programs of your own. The guides mentioned in the book are meant for machine learning programs that analyse data. You will be learning about basics of machine learning, FP growth, tree-based regression, K means clustering, Big Data, and Map Reduce, support vendor machines, and logistic regression. This is yet another book on the list that is not available in the digital format. You will have to buy the paperback version of the book in stores. 




Hands-On Machine Learning With Scikit-Learn, Keras, And Tensorflow: Concepts, Tools, And Techniques To Build Intelligent Systems


O Reilly Media mostly publishes books for advanced program users. This is yet another book from the publishing house that focuses on complex and advanced concepts. This is mainly for advanced programmers. Author Aurélien Géron does not waste time discussing basic concepts and principles in machine learning. He jumps straight to complex concepts like Scikit-Learn, Keras, and TensorFlow. 


Individuals with no programming language experience will find it challenging to understand the ideas presented in the book. You need an actual programming language to understand the concepts and apply them. The book requires readers to apply their programming knowledge in the exercises. The book is meant to help programmers develop intelligent machine learning programs to be used by companies and big corporations. If you think you lack the knowledge to develop smart software, then this book is a must-have for you. The book is available in Kindle format and paperback. You will learn the following things:

  •  deep reinforcement learning
  • training models
  • decision trees
  • deep neural networks
  • training neural nets
  • linear regression
  • random forests
  • ensemble methods
  • support vector machines. 

Not Beginner-Friendly


The Hundred-Page Machine Learning Book


A 100 pages book on machine learning written by Andriy Burkov and published by him. Although it is the last book on this list, do not dismiss the book for its less page count; in fact, you will learn a lot more in this 100-page book than any other book mentioned in this list. The book is available in all kinds of formats, from digital format to paperback and hardcover. The tiny book covers everything about machine learning. You will learn about artificial intelligence, create your Machine learning programs. Then you will know the anatomy of a learning machine, supervised learning and unsupervised learning. Finally move on to fundamentals of algorithms, other forms of learning, neural networks and deep learning. 


The book is not just recommended by us but is also recommended by eBay head of engineering, Sujit Varakhedi, and Google’s director of research Peter Norvig. Experts say that upon completion of the book, you will be able to crack any interview on machine learning. Not just that, but you can also create machine learning programs by yourself.


Although it comes highly recommended by these eminent individuals, the book is not meant for absolute beginners. If you do not have a basic understanding, you will not be able to learn anything from the book.


Not Beginner-Friendly 


These are the 11 best books on machine learning that are currently available in the market. Apart from reference books, you can also watch online videos on machine learning. Finally, you can even attend online classes on this subject and get a better idea.

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