Top 10 Best Machine Learning Books to Read in 2022


Introduction To Best Machine Learning Books

The science of Machine Learning has become essential for a future towards pure automation. Synonymous with Artificial Intelligence, Machine Learning facilitates the use of systems to analyze data, identify patterns and make decisions with little to no human intervention. It has quickly become one of the most sought-after domains of computer science.

Once you learn about Machine Learning and its potential for the future, there is really no turning back. Reading books and guides has always been one of the sure-shot ways to gain knowledge in a particular domain. However, written material has the tendency to get outdated. For this very reason, Fireblaze AI School has compiled a valuable TBR (abv. To Be Read) Best Machine Learning Books list for every Machine Learning enthusiast out there. We have a special article on Top Machine Learning Interview Questions for your interview preparation.

1. Machine Learning for Dummies

Author – John Paul

Goodreads – 3.5/5

Ideal for – Beginners, people who just want to learn concepts of machine learning

Let’s face it, any guide on learning new concepts is incomplete without one for “dummies”. This book is useful for all those who have come to understand the importance of machine learning in the future. It follows a beginner-friendly approach to make you familiar with programming tools and algorithms to familiarise yourself with machine learning. Buy Now This Book

Topics Covered in This Book

  • Understand how day-to-day activities are powered by machine learning
  • Learn to code in R using R Studio
  • Find out how to code in Python using Anaconda
  • Learn the math behind machine learning, how data is preprocessed and everything in-between.

2. Hands-On Machine Learning with Scikit-Learn and TensorFlow

Author – Aurelien Geron

Goodreads – 4.7/5

Ideal for – Intermediate+Experts

Only reading up on theory can be a trite task. “Hands-On Machine Learning with Scikit-Learn and TensorFlow” combines a good number of practical examples along with a nominal amount of theory. It explains concepts ranging from basic regression models to complex ones like convolution networks. Excellent for intermediate learners, this book helps you gain a finer understanding of tools and algorithms needed to develop intelligent systems. Buy Now This Book

Topics Covered in This Book

● Training and scaling neural networks
● Using TensorFlow to train neural nets
● Using scikit-learn for machine learning
● Applications of various machine learning methods

3. Machine Learning in Action

Machine Learning in Action

Author – Peter Harrington

Goodreads – 3.8/5

Ideal for – Beginners and intermediate learners who have programming experience (especially in Python)

Dubbed as a “written tutorial for beginner data scientists”, this book promises to enlighten you with machine learning techniques a data scientist typically uses in day-to-day work. “Machine Learning in Action” helps you understand concepts behind classification, recommendation systems, predictive modeling with the help of tactical tasks. Those already familiar with coding in Python can take the most advantage of this book. Buy Now This Book

Topics Covered in This Book

● Introduction to Machine Learning
● Using MapReduce for Big Data
● Unsupervised machine learning models like K-means clustering
● Supervised machine learning models like Logistic regression and Support vector machines

4. Deep Learning

Deep Learning

Author – Ian Goodfellow, Yoshua Bengio, and Aaron Courville

Goodreads – 4.6/5

Ideal for – Experts in Machine Learning, researchers

Deep Learning is a subset of Machine Learning which involves building an array of networks called artificial neural networks capable of processing and learning from vast amounts of data. This book embarks you on a journey filled with a broad range of deep learning concepts. So if you dub yourself as an expert in Machine Learning, this book is a must-have! Software engineers who want to implement deep learning models in their products can also give it a go. Buy Now This Book

Topics Covered in This Book

  • Mathematical background – linear algebra, automata theory, computation
  • Industry adapted techniques like Convolutional Networks, Optimisation Algorithms, Neural Networks and Sequence Mapping
  • Algorithms behind applications like Speech Recognition, NLP (Natural Language Processing), Computer Vision

5. Fundamentals of Machine Learning for Predictive Data Analytics: Algorithms, Worked Examples, and Case Studies

Fundamentals of Machine Learning for Predictive Data Analytics: Algorithms, Worked Examples, and Case Studies

Author – Aoife D’Arcy, Brian Mac Namee, and John D. Kelleher

Goodreads – 4.4/5

Ideal for – Intermediate learners in Data Analytics and Machine Learning

Predictive Data Analytics has set its footprint in every industry. Stock market prediction, banking loan classifiers, product recommendations – all these are applications of predictive modeling. This introductory textbook explains how machine learning is used in predictive data analytics. Each machine learning concept is thoroughly explained with diagrams, examples, and algorithms employed. Buy Now This Book

Topics Covered in This Book

● Techniques for evaluating machine learning models
● Different types of supervised and unsupervised learning
● Mathematical models explained with examples
● Case studies on predictive analytic

6. Machine Learning For Absolute Beginners

Machine Learning For Absolute Beginners

Author – Oliver Theobald

Goodreads – 4.4/5

Ideal for – Beginners (even with no coding experience!)

If you want to judge a book by its cover, go ahead! “Machine Learning for Absolute Beginners” is for all those who are completely new to Machine Learning. Even those with no background in mathematics and computer science can benefit from this book. The author has taken utmost care in trying to explain complex concepts with relative ease – using visual examples and ML algorithms. Buy Now This Book

Topics Covered in This Book

  • Introduction to neural networks
  • Algorithms on Clustering, Feature Engineering etc
  • Data scraping techniques
  • ML code adaptations using Python

7. Understanding Machine Learning

Understanding Machine Learning

Author – Shai Ben-David and Shai Shalev-Shwartz

Goodreads – 4.3/5

Ideal for – Machine Learning experts, PhD researchers

An ideal textbook for researchers and PhD seekers who have tested the waters of Machine Learning. “Understanding Machine Learning”  not only covers algorithms and applications of various supervised and unsupervised learning models but also digs deeper to explain the complexity of such algorithms. It provides the mathematical background as to what goes behind the construction of a particular ML algorithm. Buy Now This Book

Topics Covered in This Book

● The computational complexity of algorithms
● Concepts like stochastic gradient descent, neural networks, and structured output learning
● Upcoming theoretical concepts like the PAC-Bayes approach and compression-based bounds

8. The Hundred-Page Machine Learning Book

the hundred-page machine learning book

Author – Andriy Burkov

Goodreads – 4.1/5

Ideal for – Interview preparation, quick reference guide for beginners

An all-you-need, easy-to-comprehend book to understand machine learning topics. After reading  ”The Hundred-Page Machine Learning Book” you will have gained an adequate understanding of different types of machine learning models and their applications. That being said, it only explains the theory in brief and for a more fundamental understanding, beginners should look elsewhere. Buy Now This Book

Topics Covered in This Book

● Theory and difference between supervised and unsupervised learning
● Support vector machines,
● Neural networks and cluster analysis
● Transfer learning, feature engineering

9. Make Your Own Neural Network

Make Your Own Neural Network

Author – Tariq Rashid

Goodreads – 4.5/5

Ideal for – Intermediate learners

The intention behind writing this book was to make understanding and implementing neural networks a piece of cake. This guide begins with covering the basic idea and mathematical paradigm behind neural networks, gradually building up to its implementation. Neural networks are essential in the field of machine learning and this book enables its understanding by making them accessible. Buy Now This Book

Topics Covered in This Book

  • The idea behind neural networks with illustrations and examples
  • Using Python to implement neural network algorithms
  • Extend the idea to IoT implementations (using Raspberry Pi)

10. Machine Learning for Hackers

Machine Learning for Hackers

Author – Drew Conway and John Myles White

Goodreads – 3.7/5

Ideal for – Expert data scientists and coders (with experience in R programming)

The authors of this book help you understand machine learning and predictive tools using hands-on case studies instead of a dull theory-filled approach. Hackers here mean skilled programmers. Using the R programming language, you will gain an understanding of how to analyze popular datasets and implement the required machine learning model to generate necessary insights. Buy Now This Book

Topics Covered in This Book

  • Building recommendation systems
  • Implement statistical analysis and regression models
  • Develop naive Bayes classifier
  • Learn optimization techniques

Hope this reading Best Machine Learning Books list helps you on your journey to becoming a great Data Scientist. By reading these books, you are sure to build a strong foundation in Machine Learning. Make sure to come back to rereading books and revise concepts periodically.


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