10 Best Machine Learning Books in 2022 For Both Beginners & Experts   

From the name of it, Machine Learning (ML) may sound a little intimidating to newcomers. In addition, this particular field of computer science is quite massive and has a plethora of fields in which you can make your career. Finding the right research topic and applications to learn about is the hardest part of mastering the knowledge of machine learning. 

As a result, today, with the help of this article, we aim to provide you with a list of the top 10 best machine learning books for beginners and as well as for experts. Also, we shall walk you through a brief introduction to machine learning and its types. 

If you start with books, you will be able to learn the concepts of machine learning easily from scratch and can implement those concepts to create ML models and applications. 

However, to get started with machine learning requires learners to have prior knowledge of programming and mathematics. So if you are entirely new to computer science, you should learn programming languages like Python, C/C++, Java, R, and JavaScript. 

What is Machine Learning?

Alan Turin quoted a wonderful sentence that depicts the use of machine learning. He stated, “What we want is a machine that can learn from experience.” In 1947, his vision was to find a way to make machines learn on their own from experience. Now in the 21st century, the concept of self-learning machines has become a reality, all thanks to machine learning. 

Machine learning involves the study of computer algorithms and statistical models to complete specific tasks, which requires finding patterns and inference instead of providing explicit instructions to the system. More specifically, ML is a subset of artificial intelligence that emphasizes the use of data and algorithms to simulate the way humans learn and think. In simple terms, ML allows machines to learn from experiences and provide accurate predictions without being programmed explicitly. 

There is no doubt that machine learning jobs offer some of the highest packages in the field of computer science. Over the past two decades, we have seen considerable storage and processing power advancements. This results in the creation of some of the most innovative products, which works by understanding a user’s needs. The best example of machine learning is Netflix’s recommendation engine and also self-driving cars from Tesla. 

Types of Machine Learning

One can train a machine using different machine learning algorithms. Each algorithm has its own set of advantages and precautions. Before we find out the different types of machine learning, we first need to understand the different types of data we want to ingest for the algorithm to learn from it. 

The first is labeled data, and the second is unlabeled data. The labeled data comes with input and output parameters and is also a machine-readable pattern. But to label, the data requires manual work for a human, so it takes time. 

On the other hand, unlabeled data comes with single or no parameters in a machine-readable form. As a result, there is no need for a human to add labels to the data manually, but the way to ingest this data is a little more complex. 

1. Supervised Learning 

These are the basic machine learning methods, and here labeled data is used for the learning algorithm. The data needs to be labeled accurately for this learning method to work. Remember that this is an extremely powerful learning method; you should only use it when you are 100% sure of the circumstances. 

Here a small dataset from a large dataset is given to the ML algorithm to provide the basic idea of the problem, solution, and data points. The training dataset is similar to the final dataset in terms of characteristics. 

2. Unsupervised Learning 

With unsupervised learning, you can work with unlabeled data, meaning no human labor is required to make the dataset readable for the machine. This allows the machine to work with much larger datasets. As no labels are present, we can find hidden structures being made inside the datasets. 

In addition, the algorithm perceives relationships between data points in an abstract manner, so no input is required from your end. With the creation of these hidden structures, this form of machine learning becomes more versatile. 

3. Reinforcement Learning 

This learning method takes inspiration from how human beings learn from their life experiences. Here, the algorithm improves itself by learning from new situations via a trial-and-error method. This learning method encourages favorable outputs, also known as reinforced, are encouraged. 

At the same time, non-favorable outputs are discouraged or punished. The algorithm works in the environment with an interpreter and a reward system. In every iteration of the algorithm, the results that come as an output will be transferred to the interpreter, which then decides whether the given outcome is favorable. 

Best Machine Learning Books for Beginners and Experts

Now that you have understood the basics of ML. Let’s see some of the best ML books which will help you gain core knowledge of ML and showcase how you can implement it in your projects. 

1. Machine Learning For Absolute Beginners: A Plain English Introduction (Second Edition) by Oliver Theobald

Starting the list, we have this one book whose title is relatively self-explanatory. This is a complete introduction to machine learning for beginners, so this is an excellent place to start your ML learning journey. 

To begin learning ML from this book, you don’t require to have a mathematical background, nor do you need programming experience. This is because it is the most basic introduction to machine learning. The word “plain” has been highly valued here as the book hops over the technical jargon to present much better explanations to readers for ease of reading. 

There are many accessible explanations and visual examples to help you understand the working of different algorithms. In addition to this, you will also learn about some simple terms of programming in the context of machine learning. 

Throughout this book, you will learn about downloading free datasets, the basics of machine learning tools and libraries, creating trend lines, data scrubbing techniques, decision trees, building ML models, clustering, and neural networks. 

2. Machine Learning (in Python and R) For Dummies by John Mueller and Luca Massaron

For most machine learning implementations, engineers tend to use Python to get things done. This is one of the best machine learning books for beginners that teach them how to achieve real-world tasks. It covers all the basic topics of ML that you need to know as a beginner. In addition, you will be familiar with the languages and tools required for transforming ML-based tasks into reality.

This is a no-nonsense guide towards learning machine learning by using two of the most common languages Python and R. You will learn how to write a program in these two languages so they can make machines handle pattern-oriented tasks and analyze big data. 

3. Programming Collective Intelligence: Building Smart Web 2.0 Applications by Toby Segaran

If you have some basic knowledge of programming, learning ML from this book is going to be a piece of cake for you. This book follows the concepts of ML in a more practical way rather than showcasing the theoretical implementation of machine learning first. You will get to know how to write programs that result in machine learning algorithms and how to gather data for specific projects from websites and applications. 

Also, you will learn how to make automated filters to understand what the collected means. Moreover, you will learn a number of filtering techniques to detect groups, patterns, and the working of search engine algorithms. Also, this book tells how search engine makes predictions. 

Some major topics the book covers are search engineer features, clustering methods, optimization algorithms, decision trees, non-negative matrix factorization, and Bayesian filtering. At the end of each chapter, you will have exercises to test out your learning from the chapter. In a nutshell, this is one of the most comprehensive books on machine learning. 

4. Machine Learning for Hackers: Case Studies and Algorithms to Get You Started by Drew Conway and John Myles White

Let’s start with the disclaimer for this one ML textbook, the word “Hackers” used in the title of the book isn’t mentioning those people who are inclined to get unauthorized access to people’s data. Here the author is describing hackers as a group of people who are working on a specific project. It is one of the best books to learn machine learning for expert learners. 

To be able to get the most out of this book, you need to have working experience with programming and coding. Also, you don’t have to worry about your knowledge of mathematics and statistics for this book, and it can take the back seat. 

The book guides you through each of its chapters using real-life case studies where machine learning has been used or is currently under use to make the application self-taught. Each chapter focuses on a particular machine learning problem, such as classification, optimization, recommendation, and prediction. 

Moreover, you need to be proficient in the R language as this book helps you analyze sample datasets and write ML algorithms using R. You will learn about various optimization techniques, building a recommendation system using Twitter data, and developing a naïve Bayesian classifier for identifying spam emails. 

5. Artificial Intelligence: A Modern Approach By Stuart J.Russel & Peter Norvig

Some engineers consider it the best machine learning book of all time, and it is a de-facto machine learning guide for many people. It has the most detailed introduction to machine learning and artificial intelligence. ML and AI are considered complicated fields of computer science, but this book makes it easier for the readers to comprehend their use case, algorithms, working, and implantation in various applications. 

The 4th edition of this book has been out for just a few months now. In this latest edition, you will find all the latest features, trends, and technologies that are being used in the world of machine learning. As a result, we can say the authors have revamped the book, and there is a new take on how machine learning has grown over time which you get to read in this textbook. 

In addition, the book covers various other topics, such as robotics, natural language processing, probabilistic programming, transfer learning, multiagent systems, deep learning, and safe AI. 

6. Machine Learning: The New AI by Ethem Alpaydin

We know machine learning has a fantastic range of applications, and the numbers are going to increase each year as more products or applications are now moving towards automation. In addition to this, from recommending your product online when you open the e-commerce platform to even recognizing your voice and giving you answers to the questions, everything uses machine learning in one way or the other. The whole concept of machine learning is based on one thing, which is big data. 

With this book, you will complete the path of converting data into knowledge through machine learning in order to make it useful for your application and customers. With this book, you will learn about the concepts of ML via examples of applications where the specific concept of ML has been used. 

Besides the knowledge of machine learning, this book also covers the elements of artificial neural networks, data science, reinforcement learning, and legal implications of ML data in order to provide data privacy and security. 

7. Machine Learning in Action by Peter Harrington

It is one of the best machine learning books for beginners. It introduces readers to techniques that they need to master for the implementation of machine learning. This book is written as more of a tutorial guide to help engineers write code so that they can acquire data on their own and analyze it. It has all the necessary techniques which you need for the practical implementation of AI. 

Moreover, the programming language snippet feature code and the examples of the algorithm will help you get started with machine learning. Before you purchase this book, it is better to have some previous knowledge of Python, as this book uses it to explain most of the machine learning examples. 

The author has divided the book into four parts: Classification, Forecasting numeric values with regression, Unsupervised learning, and Additional tools. Also, the book is replete with examples of common ML tasks. You will learn how to implement classic ML algorithms, like Apriori and Adaboos. 

8. Hands-on ML with Scikit-Learn, Keras & TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems by Aurélien Géron

This book teaches readers the use of ML via libraries like Scikit-Learn, Keras & TensorFlow2. For many, this is the best introductory book one can read for machine learning. There are a number of topics that have been included in the second edition which were not present in the first edition. 

Also, the author of the book presumed that readers don’t know anything about machine learning. So he has written this book by including substantial instances, a minimal amount of theory, and two ready-to-use Python frameworks, mainly Keras & TensorFlow. Aurélien Géron will provide you with the best insights on how to create notions and tools in order for the development of smart systems. 

There are so many machine learning techniques that you are going to learn from this book. Some of them are easy linear regression, deep learning, algorithm fundamentals, end-to-end projects, and learning how to work with neural networks to attain valuable data. With each chapter, there is an exercise waiting for you at the end, which helps you understand where and how to apply the concept you have just learned. 

To start with this, all you need is a little bit of experience in programming. It is among the best books to learn machine learning. 

9. Fundamentals of Machine Learning for Predictive Data Analytics by John D. Kelleher, Brian Mac Namee, and Aoife D’Arcy

If you have learned the basics of machine learning and then want to move toward predictive data analytics in that case, this book is a godsend for you. With the help of machine learning, one can create predictive models which will extract patterns that are present in large datasets. This book showcases the concept of predictive analytics via machine learning in a much more detailed manner than any other book. 

Also, from this ML book, you will learn the four major approaches to implementing machine learning information-based learning, similarity-based learning, probability-based learning, and error-based learning. Each of these four approaches is present in the form of a non-technical conceptual explanation, and it is followed by mathematical models. Lastly, the algorithms are written in the form of illustrated and detailed examples. 

10. Mathematics for Machine Learning by Marc Peter Deisenroth

If you have started learning machine learning, there is one thing that we are sure came to your mind, and that is how important it is for the learner to have knowledge of mathematics. All the ML books we have mentioned in this list describe the concepts and working of machine learning in a beautiful way. But this is the one book that concentrates heavily on the importance of mathematics in the field of machine learning. 

This book does not talk about the complex elements of machine learning. On the contrary, it will provide you with the essential concepts of mathematics which you require for learning machine learning to its core. 

The chapters are easy to understand, and this is a must-book for those who are good at mathematics and want to learn machine learning as well. Every chapter consists of solved examples and exercises to test your knowledge. 

All the mathematical concepts you learn in this book are helpful for implementing four core ML algorithms: linear regression, principal component analysis, Gaussian mixture models, and support vector machines. 

Free Machine Learning Books

1. Neural Networks and Deep Learning

By: Michael Nielsen

It is a free online machine learning book that introduces readers to deep learning and neural networks. 

2. Pattern Recognition and Machine Learning

By: Christopher Bishop

Aimed at advanced graduates or first-year students of Ph.D., this book provides a comprehensive introduction to pattern recognition and machine learning. 

By: Daoud Clarke

It is one of the best free machine learning books for project managers and software engineers having zero knowledge of machine learning. 

4. Foundation to Machine Learning

By: Mehryar Mohri, Afshin Rostamizadeh, and Ameet Talwalkar

This book covers all the mathematical concepts you need to learn machine learning. 

You can download this ML book’s PDF here. 

Conclusion

So these were some of the best-recommended books in the field of machine learning. You can select the books as per your topics and the field. In most cases, these machine learning textbooks will help you in creating the necessary foundation of understanding for your knowledge. These textbooks are known to be the best in the market in this highly advanced and rapidly growing stream of computer science. 

Frequently Asked Questions

1. Is it possible to learn ML from a book? 

Yes, ML can be learned by using books written on machine learning. Machine learning is a little bit advanced subject in computer science, but with the help of books, you can pace your learning process at your own speed and comfort. 

2. Which is the best ML book for beginners? 

There are a number of absolutely amazing books available for you to learn ML from scratch, so it is hard to pinpoint the one which caters to the needs of the masses. Still, if we have to choose an ML book that is best for beginners, then we would recommend reading Machine Learning For Absolute Beginners: A Plain English Introduction (2nd edition). 

3. Which is the Bible of Machine Learning?

There is no one book that can teach you all the concepts of machine learning. At the same time, you need to join courses, take online lectures, and attend seminars to attain as much information as possible on machine learning. Even after reading 5 books from our list of best books to learn machine learning, you still might be missing some concepts which only machine learning developers can tell you about. So indeed, there is no bible for machine learning.

4. Is it difficult to learn ML?

There is no straight answer, yes or no, to this question. For some people, ML might pose a complex challenge for learning. At the same time, others who have past experience in programming will find it easy. We can say for complete beginners of computer science, and ML is a tough choice to start with. On the other hand, for experienced programmers, ML isn’t hard. 

5. What are the types of ML?

There are three main methods of machine learning, and semi-supervised machine learning is a subset of supervised machine learning. 

• Supervised Machine Learning
• Unsupervised Machine Learning
• Semi-Supervised Machine Learning
• Reinforcement Learning

Vijay Singh Khatri

Graduate in Computer Science, specialized in Digital Marketing. I am very fond of writing tech articles and creating my own blog to teach my audience.

learndunia.com