20 Best Books on Machine Learning You Must Read

By the way, we are all aware of how quickly machine learning technology has developed in the last several years. Machine learning is a discipline that has attracted the interest of several corporations, academics, and sectors.

Due to this, I will discuss some of the greatest books on machine learning that an engineer or newbie should read today. You must all have agreed that reading books is not the same as using the intellect.

Reading books helps our minds discover a lot of new things. Reading is learning, after all. A self-learner tag is a lot of fun to have. The greatest textbooks available in the field will be highlighted in this article.

The following textbooks offer a tried-and-true introduction to the larger field of AI and are often used in university courses and recommended by academics and engineers alike.

Even if you have a ton of machine learning experience, picking up one of these textbooks might be a terrific way to brush up. After all, learning is a continuous process.

1. Machine Learning For Absolute Beginners

You’d like to study machine learning but don’t know how to do it. There are several crucial theoretical and statistical concepts you should understand before starting your epic trip into machine learning. And this book fills that need!

It offers complete novices with a high-level, applicable introduction to machine learning. The book Machine Learning for Absolute Beginners is one of the best choices for anyone searching for the most simplified explanation of machine learning and associated ideas.

The book’s numerous ml algorithms are accompanied by concise explanations and graphic examples to help readers understand everything that is discussed.

Topics covered in the book

• Basics of neural networks
• Regression analysis
• Feature engineering
• Clustering
• Cross-validation
• Data scrubbing techniques
• Decision Trees
• Ensemble modeling

2. Machine Learning for Dummies

Machine learning might be a confusing idea for regular people. However, it is priceless to those of us who are knowledgeable.

Without ML, it is hard to manage issues like online search results, real-time advertisements on web pages, automation, or even spam filtering (Yeah!).

As a result, this book offers you a straightforward introduction that will help you learn more about the enigmatic realm of machine learning. With the aid of Machine Learning For Dummies, you will learn how to “speak” languages like Python and R, which will enable you to train computers to do pattern recognition and data analysis.

Additionally, you’ll learn how to use Python’s Anaconda and R Studio to develop in R.

Topics covered in the book

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

3. The Hundred Page Machine Learning Book

Is it feasible to cover all aspects of machine learning in under 100 pages? Andriy Burkov’s The Hundred-Page Machine Learning Book is an attempt to do the same.

The machine learning book is well-written and supported by renowned thought leaders including Sujeet Varakhedi, Head of Engineering at eBay, and Peter Norvig, Director of Research at Google.

It is the greatest book for a beginner in machine learning. After thoroughly reading the book, you will be able to construct and understand sophisticated AI systems, succeed in a machine learning interview, and even launch your very own ML-based company.

However, the book is not intended for complete beginners in machine learning. Look someplace if you’re seeking anything more fundamental.

Topics covered in the book

• Anatomy of a learning algorithm
• Supervised learning and unsupervised learning
• Reinforcement Learning 
• Fundamental algorithms of Machine Learning 
• Overview of Neural networks and deep learning

4. Understanding Machine Learning

A systematic introduction to machine learning is provided in the book Understanding Machine Learning. The book delves deeply into the foundational ideas, computational paradigms, and mathematical derivations of machine learning.

An extensive range of machine learning subjects is presented in a simple manner by machine learning. The theoretical foundations of machine learning are described in the book, together with the mathematical derivations that turn these foundations into useful algorithms.

The book presents the fundamentals before covering a wide range of crucial subjects that haven’t been covered by earlier textbooks.

Included in this are a discussion of the convexity and stability concepts and the computational complexity of learning, as well as significant algorithmic paradigms like stochastic gradient descent, neural networks, and structured output learning, as well as newly emerging theoretical ideas like the PAC-Bayes approach and compression-based bounds. designed for beginning grads or advanced undergraduates.

Topics covered in the book

• The computational complexity of machine learning
• ML algorithms
• Neural networks
• PAC-Bayes approach
• Stochastic gradient descent
• Structured output learning

5. Introduction to Machine Learning with Python

Are you a Python-savvy data scientist who wants to study machine learning? The best book to start your machine learning adventure with is Introduction to Machine Learning with Python: A Guide for Data Scientists.

With the help of the book Introduction to Machine Learning with Python: A Guide for Data Scientists, you will discover a variety of useful techniques for creating custom machine learning programs.

You will cover every crucial step involved in utilizing Python and the Scikit-Learn package to build dependable machine learning applications.

Gaining a solid grasp of the matplotlib and NumPy libraries will make learning much easier.

Topics covered in the book

• Modern techniques for parameter tweaking and model assessment
• Applications and basic machine learning ideas
• automated learning techniques
• Techniques for manipulating text data
• Model chaining and workflow encapsulation pipelines
• Data representation after processing

6. Hands-on Machine Learning with Sci-kit learn, Keras & Tensorflow

Among the most thorough publications on data science and machine learning, it is stuffed full of knowledge. It is advised that experts and novices alike study more about this subject.

Although this book contains just a little amount of theory, it is supported by strong examples, giving it a spot on the list.

This book includes a variety of topics, including scikit-learn for machine learning projects and TensorFlow for creating and training neural networks.

Following reading this book, we think you’ll be better equipped to delve further into deep learning and deal with practical problems.

Topics covered in the book

• Examine the landscape of machine learning, especially neural networks
• Track a sample machine learning project from beginning to conclusion using Scikit-Learn.
• Examine several training models, such as ensemble techniques, random forests, decision trees, and support vector machines.
• Create and train neural networks by utilizing the TensorFlow library.
• Consider convolutional networks, recurrent nets, and deep reinforcement learning while exploring neural net designs.
• Learn how to scale and train deep neural networks.

7. Machine Learning for Hackers

For the seasoned programmer interested in data analysis, the book Machine Learning for Hackers is written. Hackers are skilled mathematicians in this context.

For someone with a solid understanding of R, this book is a great choice because the majority of it is centered on data analysis in R. Additionally covered in the book is how to manipulate data using advanced R.

The inclusion of pertinent case stories emphasizes the value of employing machine learning algorithms can be the book Machine Learning for Hackers’ most significant selling point.

The book gives many real-world examples to make learning machine learning simpler and faster rather than going deeper into its mathematical theory of it.

Topics covered in the book

• Create a naive Bayesian classifier that analyzes simply the content of an email to determine whether it is spam.
• Predicting the number of page views for the top 1,000 websites using linear regression
• Investigate optimization methods by attempting to crack a straightforward letter cipher.

8. Python Machine Learning with Examples

This book, which helps you comprehend and create various Machine Learning, Deep Learning, and Data Analysis methods, is likely the only one that focuses only on Python as a programming language.

It covers several potent libraries for implementing different Machine Learning algorithms, such as Scikit-Learn. The Tensor Flow module is then used to teach you about deep learning.

Finally, it demonstrates the many data analysis opportunities that can be achieved using machine and deep learning.

It also teaches you the numerous techniques that can be utilized to increase the effectiveness of the model you create.

Topics covered in the book

• Learning Python and Machine Learning: A Beginner’s Guide
• Examining the 2 newsgroups data set and Naive Bayes spam email detection
• Using SVMs, classify the topics of news stories Click-through prediction using algorithms based on trees
• Prediction of click-through rate using logistic regression
• The use of regression algorithms to forecast stock prices’ highest standards

9. Python Machine Learning

The Python Machine Learning book explains the fundamentals of machine learning as well as its significance in the digital domain. It is a machine learning book for beginners.

Additionally covered in the book are machine learning’s many subfields and applications. The principles of Python programming and how to get started with the free and open-source programming language are also covered in the Python Machine Learning book.

After finishing the machine learning book, you will be able to effectively establish a number of machine learning jobs using Python coding.

Topics covered in the book

• Artificial intelligence fundamentals
• a decision tree
• Logistic regression
• In-depth neural networks
• Python programming language fundamentals

10. Machine Learning: A Probabilistic Perspective

Machine Learning: A Probabilistic Perspective is a humorous machine learning book that features nostalgic color graphics and practical, real-world examples from disciplines such as biology, computer vision, robotics, and text processing.

It is full of casual prose and pseudocode for essential algorithms. Machine Learning: A Probabilistic Perspective, in contrast to other machine learning publications that are presented in the style of a cookbook and describe various heuristic approaches, focuses on a principled model-based approach.

It specifies ml models using graphical representations in a clear and understandable manner. Based on a unified, probabilistic approach, this textbook provides a complete and self-contained introduction to the area of machine learning.

The content is both broad and deep, including fundamental background material on topics such as probability, optimization, and linear algebra, as well as a discussion of contemporary advancements in the area such as conditional random fields, L1 regularization, and deep learning.

The book is written in a casual, approachable language, containing pseudo-code for the main significant algorithms.

Topics covered in the book

• Probability
• Deep learning
• L1 regularization
• Optimization
• Text processing
• Computer Vision applications
• Robotics applications

11. The Elements of Statistical Learning

For its conceptual framework and a wide variety of subjects, this machine learning textbook is often acknowledged in the field.

This book can be used as a reference for anybody who needs to brush up on topics like neural networks and testing techniques as well as a simple introduction to machine learning.

The book aggressively pushes the reader to do their own experiments and investigations at every turn, making it valuable for cultivating the abilities and curiosity required to make pertinent advancements in a machine learning capacity or job.

It is an important tool for statisticians and anybody interested in data mining in business or science. Make sure you understand linear algebra at a minimum before beginning this book.

Topics covered in the book

• Supervised learning (prediction) to unsupervised learning
• Neural networks
• Support vector machines
• Classification trees
• Boosting algorithms

12. Pattern Recognition and Machine Learning

The worlds of pattern recognition and machine learning can be thoroughly explored in this book. The Bayesian approach to pattern recognition was originally presented in this publication.

Furthermore, the book examines challenging subjects that need a working understanding of multivariate, data science, and fundamental linear algebra.

On machine learning and probability, the reference book offers chapters with progressively harder levels of complexity based on trends in datasets. Simple examples are given before a general introduction to pattern recognition.

The book offers techniques for approximate inference, which allow quick approximations in cases when exact solutions are impractical. There are no other books that employ graphical models to describe probability distributions, but it does.

Topics covered in the book

• Bayesian methods
• Approximate inference algorithms
• New models based on kernels
• Introduction to basic probability theory
• Introduction to pattern recognition and machine learning

13. Fundamentals of Machine Learning from Predictive Data Analytics

If you’ve mastered the fundamentals of machine learning and want to move on to predictive data analytics, this is the book for you!!! By finding patterns from massive datasets, Machine Learning can be used to develop prediction models.

This book examines the implementation of ML utilizing Predictive Data Analytics in-depth, including both theoretical principles and actual examples.

Despite the fact that the title “Fundamentals of Machine Learning for Predictive Data Analytics” is a mouthful, this book will outline the Predictive Data Analytics journey from data to insight to a conclusion.

It also discusses four machine learning approaches: information-based learning, similarity-based learning, probability-based learning, and error-based learning, each with a non-technical conceptual explanation followed by mathematical models and algorithms with examples.

Topics Covered in the book

• Information-based learning
• Similarity-based learning
• Probability-based learning
• Error-based learning

14. Applied Predictive Modelling

Applied Predictive Modeling examines the whole predictive modeling process, beginning with the critical phases of data preprocessing, data splitting, and model tuning foundations.

The work then presents clear descriptions of a variety of conventional and recent regression and classification approaches, with a focus on showing and solving real-world data challenges.

The guide demonstrates all aspects of the modeling process with several hands-on, real-world examples, and each chapter includes comprehensive R code for each stage of the process.

This multipurpose volume can be used as an introduction to predictive models and the whole modeling process, as a reference guide for practitioners, or as a text for advanced undergraduate or graduate level predictive modeling courses.

Topics covered in the book

• Regression technique
• Classification technique
• Complex ML algorithms

15. Machine Learning: The Art and Science of Algorithms that Make Sense of Data

If you are an intermediate or expert in machine learning and want to go “back to the fundamentals,” this book is for you! It pays full credit to Machine Learning’s enormous complexity and depth while never losing sight of its unifying principles (quite an accomplishment!).

Machine Learning: The Art and Science of Algorithms include several case studies of increasing complexity, as well as numerous examples and pictures (to keep things interesting!).

The book also covers a wide range of logical, geometric, and statistical models, as well as complicated and novel subjects like matrix factorization and ROC analysis.

Topics covered in the book

• Simplifies machine learning algorithms
• Logical model
• Geometric model 
• Statistical model
• ROC analysis

16. Data Mining: Practical Machine Learning Tools & Techniques

Using approaches from the study of database systems, machine learning, and statistics, data mining techniques enable us to find patterns in vast amounts of data.

You should get the book Data Mining: Practical Machine Learning Tools and Techniques if you need to study data mining techniques in particular or plan to learn machine learning in general.

The best book on machine learning concentrates more on its technical side. It delves further into machine learning’s technical intricacies, and strategies for gathering data and using various inputs and outputs to judge outcomes.

Topics covered in the book

• Linear models
• Clustering
• Statistical modeling
• Predicting performance
• Comparing data mining methods
• Instance-based learning
• Knowledge representation & clusters
• Traditional and modern data mining techniques

17. Python for Data Analysis

The ability to evaluate the data used in machine learning is the most important skill a data scientist must possess. Before developing an ML model that produces an accurate forecast, the majority of your job will include handling, processing, cleaning, and assessing data.

You need to be familiar with programming languages like Pandas, NumPy, Ipython, and others in order to execute data analysis.

If you want to work in data science or machine learning, you must have the ability to manipulate data.

You should definitely read the book Python for Data Analysis in this case.

Topics covered in the book

• Essential Python Libraries
• Advanced Pandas
• Data Analysis Examples
• Data Cleaning and Preparation
• Mathematical and Statistical Methods
• Summarizing and Computing Descriptive Statistics

18. Natural Language Processing with Python

The foundation of machine learning systems is natural language processing.

The book Natural Language Processing with Python instructs you on how to utilize NLTK, a well-liked collection of Python modules and tools for symbolic and statistical natural language processing for English and NLP in general.

The Natural Language Processing with Python book provides effective Python routines that demonstrate NLP in a concise, obvious way.

Readers have access to well-annotated datasets for dealing with unstructured data, text-linguistic structure, and other NLP-focused elements.

Topics covered in the book

• How does human language function?
• Linguistic data structures
• Natural Language Toolkit (NLTK)
• Parsing and semantic analysis
• Popular linguistic databases
• Integrate techniques from artificial intelligence and linguistics

19. Programming Collective Intelligence

The Programming Collective Intelligence by Toby Segaran, which is regarded as one of the greatest books to start understanding machine learning, was written in 2007, years before data science and machine learning attained their current position as leading professional paths.

The book uses Python as the method for disseminating its expertise to its audience. The Programming Collective Intelligence is more of a manual for ml implementation than it is an introduction to machine learning.

The book provides information on developing effective ML algorithms for gathering data from apps, programming for obtaining data from websites, and extrapolating the data collected.

Each chapter includes activities for expanding the discussed algorithms and enhancing their usefulness.

Topics covered in the book

• Bayesian filtering
• Support vector machines
• Search engine algorithms
• Ways to make predictions
• Collaborative filtering techniques
• Non-negative matrix factorization
• Evolving intelligence for problem-solving
• Methods for detecting groups or patterns

20. Deep Learning (Adaptative Computation and Machine Learning Series)

As we are all aware, deep learning is an improved kind of machine learning that enables computers to learn from past performance and a large amount of data.

While using machine learning techniques, you need also be conversant with deep learning principles. This book, which is regarded as the Bible of deep learning, will be very helpful in this circumstance.

Three deep learning experts cover highly complicated topics that are filled with mathematics and deep generative models in this book.

Providing a mathematical and conceptual basis, the work discusses pertinent ideas in linear algebra, probability theory, information theory, numerical computation, and machine learning.

It examines applications like natural language processing, speech recognition, computer vision, online recommendation systems, bioinformatics, and videogames and describes deep learning techniques used by industry practitioners, such as deep feedforward networks, regularization, and optimization algorithms, convolutional networks, and practical methodology.

Topics covered in the book

• Numerical Computation
• Deep Learning Research
• Computer Vision techniques
• Deep Feedforward Networks
• Optimization for Training Deep Models
• Practical Methodology
• Deep Learning Research

Conclusion

The 20 top machine learning books are summarized in that list, which you can use to progress machine learning in the direction you like.

You’ll be able to develop a solid foundation in machine learning expertise and a reference library that you can use often while working in the area if you read a variety of these textbooks.

You’ll be inspired to keep learning, getting better, and having an effect even if you just read one book.

When you’re prepared and competent to develop your own machine learning algorithms, keep in mind that data is vitally essential to the success of your project.