Machine learning allows humans to automate tasks. Machine learning is the study of computer algorithms and statistical models to perform a task that uses patterns and inferences rather than explicit instructions.

Did you know that Machine Learning is a hot career option? Research shows that Machine Learning Engineers are the most popular job in 2021. They have a growth rate of 344%, an average salary of $146,000.85 per year, and a high level of pay.

Machine learning is an extremely difficult field. However, that doesn’t mean you can’t learn it. We have assembled a list of 21 machine learning books that you can use in your daily life.

1. Introduction to Machine Learning using Python

The Introduction To Machine Learning With Python: A Guide For Data Scientists is a great book for data scientists who are proficient in Python.

This book will teach you how to create your own machine-learning methods. This book will teach you how to build robust machine learning apps using Python and Scikit–learn libraries. You will also gain a deeper understanding of the NumPy and matplotlib libraries to speed up your learning.

This is the reference source:

• Advanced methods for parameter tuning and model evaluation

• Pipelines for encapsulating workflows and chaining models

• Machine learning basics and application

• How to work with text data

• Representation of processed information

• Machine learning algorithms

2. Machine Learning in Action with Scikit-Learn Keras and TensorFlow

The second edition of the machine learning book adds Keras to its content list, as well as TensorFlow (and Scikit-learn) This book provides a clear understanding of the various concepts and tools you need to build intelligent systems.

To understand the Hands-on Machine Learning book, you will need to have programming experience. Each chapter contains many exercises to help you put what you’ve learned into practice.

The book covers the following topics:

• Deep neural networks

• Training models include decision trees, random forests and support vector machines. Ensemble methods are also available

• Training neural nets

• Linear regression

• Deep reinforcement

3. Deep Reinforcement Learning Hands-on

Maxim Lapan, a deep-learning enthusiast, is interested in the practical applications of Deep Reinforcement Learning. This book examines the latest DL tools as well as their limitations. The book provides an opportunity for readers to understand the evaluation methods, including Cross-entropy and policy gradients. Examples are given on Atari sets of family favorites as well as virtual games such Connect4.

This book is a great resource for learning:

• Evaluate RL methods such as Cross-entropy and PPO, TRPO, PO, Actor-Critics, D4PG, QN, DDPG, Actor-Critics, and more

• To train stock trading agents, create an OpenAI environment

• Ask your agent to play Connect4 with AlphaGo Zero

• Get the most recent RL research about topics such as AI-driven chatbots

• Use the value iteration method to defeat Atari arcade games

4. Machine Learning for Absolute Beginners

If you have never had any experience with machine learning, but are interested in it, You should read the Machine learning for Absolute Beginners ebook. This reference is suitable for anyone with no coding or math background.

This book will help readers understand machine learning and other related concepts in a simplified way. To make the book easy to follow, readers will find clear explanations, visual examples, and a variety of ML algorithms.

This reference source includes:

• Clustering

• Feature engineering

• Regression analysis

• Cross-validation

• The fundamentals of neural networks

• Ensemble modeling

• Data scrubber techniques

5. The New AI: Machine Learning

Machine Learning has a wide range of applications, including product recommendations, voice recognition, and self-driving vehicles. Machine learning relies on data. Data has become more complex, making machine learning essential in turning data into knowledge.

Machine Learning-The New AI Book focuses on fundamental machine learning. It covers everything from evolution to essential learning algorithms, and examples of their applications.

It also includes:

• Machine learning algorithms for pattern identification

• Machine learning and data security: ethical and legal implications

• Artificial neural networks

• Reinforcement learning

6. Machine Learning Fundamentals for Predictive Data Analytics

This book is essential for anyone who has already learned the basics of machine learning and wants to learn more about Predictive Data Analytics. Machine learning can be used to generate predictive models from patterns in large data sets. This book provides an in-depth analysis of the application of machine learning, combining theoretical concepts with practical applications. The author also talks about the Predictive Analytics trajectory from data to insight and decision.

Fundamentals of Machine Learning in Predictive Data Analytics Book also includes four different methods of machine learning.

• Information-based learning

• Probability-based learning

• Learning based on errors

• Learning based on similarity

Each one is followed by a nontechnical explanation, mathematical models and algorithms.

7. Machine Learning for Dummies

It seems impossible to manage things like web search results and automation without machine learning. This book will help you get started on your machine learning journey.

Machine Learning For Dummies allows you to “speak” certain languages such as R or Python. You will then be able to set up machines for data analysis and pattern-oriented tasks. You can also learn to code in Python with Anaconda or R using R Studio.

The book includes the following topics:

• How machines learn

• Preparing your learning material

• Get started with math basics

• Learn from big and smart data

• Learning to solve real problems

8. Machine Learning and Pattern Recognition

This book will allow you to dive deep into the worlds of Machine Learning and Pattern Recognition. This book is the first to introduce the Bayesian perspective on pattern recognition. The book also analyzes complex topics that require knowledge of multivariate and basic linear algebra.

This reference book contains chapters of increasing difficulty on machine learning and probabilities according to patterns in data. The book begins with a general introduction to Pattern Recognition and then goes on to provide simple examples.

9. Machine Learning for Hackers

Let’s clarify that Hacker is an excellent programmer and not a secretive computer hacker.

This paperback is ideal for programmers who are interested in data crunching. This paperback helps you get started with machine learning by presenting multiple case studies rather than boring math-heavy presentations.

Machine Learning for Hackers illustrates certain issues in each chapter such as prediction, classification, and recommendation. This book teaches you how to analyze large datasets and build simple machine learning algorithms using the R programming language.

10. Programming Collective Intelligence

This book will help you understand and harness the power of search rankings, social bookmarkings, product recommendations, online matchmaking, and product recommendations. This book demonstrates how to create multiple Web 2.0 applications to tap into the vast amount of data generated by the Internet’s 3 billion users.

Programming Collective intelligence uses machine learning to help you understand user experience, personal taste and marketing. All of the machine learning algorithms can be used on any device, such as your website, blog or Wiki.

Some topics are covered in the book:

• Introduction to Collective Intelligence

• Ranking and searching

• Optimization

• Modeling with decision trees

• Building price models

• Finding independent features

11. Machine Learning

The machine learning book is recommended for intermediate and expert level users who want to go “back to basics”.

Machine Learning, The Art and Science of Algorithms offers a range of case studies that are complex and illustrates the process. The book also contains logical, mathematical, and statistical models, as well as new topics such ROC analysis or matrix factorization.

The book covers the following topics:

• Machine learning: What are the ingredients?

• Binary classification and related tasks

• Beyond binary classification

• Rule models

• Tree models

• Linear models

12. Natural Language Processing with Python

Machine learning systems include natural language processing. The Natural Language Processing With Python book teaches you how to use NLTK, a popular Python library and program for statistical and symbolic natural languages processing in English and NLP.

The book contains robust Python codes that demonstrate NLP in an easy way. The book provides a way for readers to access well-annotated datasets and analyze and solve linguistic patterns in text and unstructured data.

It covers:

• How language works in the human race

• Structures of linguistic data

• Parsing and semantic analysis

• Popular linguistics databases

• Natural Language Toolkit (NLTK)

• Incorporate techniques from artificial intelligence, linguistics

13. The Elements of Statistical Learning

The Elements Of Statistical Learning: Data Mining and Inference is a must-read for anyone who loves statistics and wants to understand machine learning through the lens of statistics. This reference book focuses on mathematical derivations that can be used to identify the basic logic of machine learning algorithms. Be sure to have a basic understanding of linear algebra before reading this book.

The book covers the following topics:

• Ensemble learning

• Averaging and model inference

• Random forests

• Regression and classification using linear approaches

• Problems with high-dimensionality

• Unsupervised and supervised learning

• Neural networks

14. Machine Learning from a Probabilistic Perspective

The Machine Learning: a Probabilistic Perspective is a machine learning book with informal writing and pseudocode that explains key algorithms. It contains nostalgic color pictures as well as practical, hands-on examples from different domains like computer vision, botany, and text processing.

This book focuses on a principled, model-based approach. This book uses graphic models to accurately define machine learning models.

This reference book contains:

• Conditional random fields

• Optimization

• Deep learning

• Probability

• Regularization of L1

15. Python Machine Learning

Python Machine Learning explains the basics of machine learning and their importance in the digital world. This book covers machine learning in all its branches and the wide variety of applications.

The author also discusses Python developers programming, and how to get started with the open-source and free programming language. After you have finished this book on machine learning, you will be able to code in Python to build a variety of machine learning tasks.

The book contains:

• The fundamentals of artificial Intelligence

• The basics of Python programming language

• Regression in logistics

• Decision trees

• Deep neural networks

16. Bayesian Reasoning, Machine Learning

Bayesian Reasoning is essential reading for anyone who is interested in machine learning. This book is a great resource for computer scientists who are interested in machine learning, but don’t have any prior knowledge of linear algebra or calculus.

David Barber offers a variety of clear examples and exercises that are suitable for both undergraduate and graduate students in computer science. The book also includes online resources as well as a comprehensive software package with demos and teaching materials.

The book contains:

• Estimated interference

• Learning from probabilistic models

• Probabilistic reasoning

• The framework for graphical models

• The Naive Bayes algorithm

• Dynamic models

17. Machine Learning in Action

Machine Learning in Action has been a favorite machine learning book for many, from students to professionals. It contains machine learning techniques, as well as their underlying concepts, in a concise format.

Developers can use this documentation as a guide to creating their own meaningful programs. The author also focuses on the algorithm that forms the basis for different machine learning techniques.

The book contains:

• Machine learning basics

• K-means clustering

• Regression based on trees

• MapReduce and Big Data

• Logistic regression

• FP-growth

• Support vector machines

18. TensorFlow for Machine Learning

TensorFlow is an open-source symbolic math library. It can also be found in the top data science Python libraries that are used for machine-learning applications. The Machine Learning With TensorFlow Book provides a detailed explanation of machine-learning concepts and hands-on code experience.

The book also discusses machine learning basics with traditional classifications, clusterings, and prediction algorithms. Deep learning concepts are covered, which allow readers to use the open-source TensorFlow library to perform any type of machine-learning task.

The book covers the following topics:

• Convolutional, recurrent, reinforcement neural networks

• Hidden Markov models

• Auto encoders

• Deep learning

• Reinforcement learning

• Linear regression

19. Understanding Machine Learning

The Understanding Machine Learning provides a structured introduction into machine learning. This book explains the fundamental theories and algorithms of machine learning, as well as mathematical derivations.

It also covers a wide range of topics related to machine learning in an easy-to understand format. This reference source is suitable for all levels of computer science students, as well as non-expert readers in mathematics, engineering, and computer science.

There are many topics covered in the book:

• Pac-Bayes method

• Stability and convexity

• Machine learning algorithms

• Structured output learning

• Neural networks

• Stochastic gradient descent

• Learning is computationally complex

20. Data Mining

Data mining can be used to find patterns in large data sets using approaches that are related to statistics, database systems, and machine learning. You might be interested in data mining techniques and machine-learning. The Data Mining book will help you.

This book focuses on the technical aspects of machine learning. This book focuses on the technical aspects of machine learning, including methods to acquire data and how to use different inputs and outputs to evaluate results.

The paperback covers the following topics:

• Clustering

• Linear models

• Learning by example

• Modern and traditional data mining techniques

• Predicting performance

• Comparing data mining methods

• Statistical modeling

• Knowledge representation & clusters

21. The Hundred-page Machine Learning Book

The Hundred Page Machine Learning Book is a 100-page book that can cover multiple topics in machine learning. The book is trusted by prominent thought leaders like Sujeet Varajkhedi (Head of Engineering at eBay) and Peter Norvig (Director of Research at Google).

You will be able to create and appreciate complex AI systems, conduct an ML-based interview and even manage your ML business after reading the book.

Some topics are covered in the book:

• Basic algorithms

• Deep learning and neural networks

• Anatomy of a Learning Algorithm

• Unsupervised and supervised learning

• Learning in other forms

Wrapping up

We have listed the 21 most recommended machine learning books to help you advance in this field. Machine learning is an increasingly popular job. It’s time to get involved in machine learning and make a career of it.

You can share your thoughts with others by commenting on books you’ve read. We are happy to hear from readers.