10 Best Machine Learning Books To Read in 2022

Even wonder to choose which best machine learning books to read? Want to get start your carrier in machine learning? Or searching for some great books to learn? Then in today’s content, we goanna remove all these confusions.

Almost thousands of machine learning books are available in the market. Which one should you read is a difficult question? So, we made a quick research to compile some of the best machine learning books to start learning. We discussed their features plus the quick view of those books.

With the rise in artificial intelligence, its crucial to learn at least the basics of machine learning systems. In today’s article, we gonna discuss the best 10 machine learning books. We sorted them for novice to advance users. So, if you are a novice then start by reading from the first book and then move next. So, let’s dig dive in machine learning books:

Machine Learning for Dummies

Written By: John Paul Mueller and Luca Massaron

Machine learning is a puzzling game for the majority of people. The book covers entry-level topics, programming languages, and tools you need to get started with machine learning. The guide makes it easier to understand and implement machine learning. It aims at accomplishing practical tasks by machine learning. By using Python and R code to train machines in performing pattern-oriented tasks and data analysis. Without ML automation, web search results, real-time ads, credit scoring, and email spam filtering are impossible.

Topics covered

• Data preparation
• Machine learning techniques
• Supervised and unsupervised learning
• Machine learning cycle
• Training ML systems
• Tying machine learning methods to outcomes

The Hundred-Page Machine Learning Book

Written By: Andriy Burkov

That’s from one of the best machine learning books to learn the crucial concepts of machine learning. Machine learning authorized by researchers at Google.  Easy to read a book by Burkov enables dummies to build complex AI systems. After reading novice can clear machine learning interviews and can start their ML-based business. Burkov picked the best topics in understanding machine learning: from theory to algorithms that are useful for practitioners. Want more clarification? Go for some other books too.

“A great introduction to machine learning from a world-class practitioner.”

By Karolis Urbonas

Topics covered

• Frameworks of a learning algorithm
• Fundamental algorithms
• Neural networks and deep learning
• Supervised learning
• Unsupervised learning

Python Machine Learning

Written By: Raschka and Mirjalili

From the best machine learning books, this covers the basics to advanced concepts of machine learning and deep learning. Refined explanation enhances the practice of machine learning through python. 3^rd editions TensorFlow 2, GAN models, reinforcement learning, and machine learning techniques. Lots of practical examples to build their complex models.

Topics Covered:

1. Master the frameworks, models, and techniques
2. Training Simple ML Algorithms for Classification
3. ML Classifiers Using scikit-learn
4. TensorFlow for deep learning
5. Image classification, sentiment analysis
6. Compressing Data via Dimensionality Reduction
7. Build and train neural networks
8. Classifying Images with Deep Convolutional Neural Networks
9. Generative Adversarial Network
10. Keras API features
11. best practices tuning models
12. Regression analysis
13. In-depth knowledge of sentiment analysis

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Natural Language Processing with Python

Written By: Steven Bird, Ewan Klein, and Edward Loper

Natural Language Processing acts as a bone for machine learning systems. NLP supports a variety of technologies, from the predictive text, email filtering, automatic summarization, and translation. The book uses Python programming language to guide you properly into using Natural Language Took Kit (NLTK).

You’ll learn the main algorithms for analyzing the content and structure of written communication. People interested in analyzing multilingual sources, unstructured data, linguistic structure in text, or developing web applications will find this book useful. It presents powerful Python codes & demonstrates NLP in a precise manner.

Topics covered

1. How human language works
2. Extract information from unstructured text
3. Analyze linguistic structure in text
4. Parsing and semantic analysis
5. Linguistic databases i.e. WordNet and treebanks
6. Natural Language Toolkit (NLTK)
7. Parsing and semantic analysis
8. Popular linguistic databases

Newbies who have already completed machine learning projects and want more precise knowledge. Then the upcoming mentioned ai books are for them.

Programming Collective Intelligence: Building Smart Web 2.0 Applications

Written By: Toby Segaran

Breaking down complex machine-learning algorithms into clear examples that can be directly applied for practical analysis. Precise knowledge about building a Web 2.0 application to dig the gold mind of vast data. You can also write smart programs to access interesting datasets from other web sites. Featured exercises improve the effectiveness of the mentioned algorithms.

Having in-depth knowledge of creating competent ML algorithms for gathering data from applications, accessing data by web scrapping, and recognizing the collected data. It covers more knowledge to applying machine learning rather than just having introduction.

Bravo! I cannot think of a better way for a developer to first learn these algorithms and methods, nor can I think of a better way for me to strengthen my knowledge.

By Dan Russell, Google

Topics covered:

1. Bayesian filtering
2. Clustering methods to detect groups
3. Collaborative filtering techniques
4. Evolving intelligence for problem-solving
5. Search engine features & Optimization algorithms
6. Non-negative matrix factorization
7. Support vector machines
8. Ways to make predictions
9. Using decision trees for effective prediction
10. Evolving intelligence for problem-solving

Data Mining: Practical Machine Learning Tools and Techniques

Written By: Ian H. Witten, Eibe Frank, and Mark A. Hall

Data mining techniques help us discover patterns in large data sets. The book advises on applying tools and techniques in real-world data mining situations. It dives deeper into the technical details of machine learning, methods for obtaining data, and using different inputs and outputs for evaluating results.

The book contains the popular WEKA machine learning software. Leading techniques coupled with the methods at the leading edge. Planning to learn data mining techniques and machine learning then you must pick up the Data Mining: Practical Machine Learning Tools and Techniques book.

Topics covered

1. Clustering
2. Comparing data mining methods
3. Instance-based learning
4. Online Appendix on the Weka workbench
5. Knowledge representation & clusters
6. Linear models
7. Applying the tools and techniques to data mining projects
8. Tips and techniques for performance improvement
9. Statistical modeling
10. Includes a downloadable WEKA software toolkit
11. Traditional and modern data mining techniques
12. Includes open-access online courses

Pattern Recognition and Machine Learning

Written By: Christopher M. Bishop

A brilliant book for understanding statistical techniques in machine learning and pattern recognition. From the machine learning books, I would highly encourage to read this. Influences graphic models in a unique way of describing probability distribution. It presents fast approximate answers in impossible conditions. Previous knowledge of pattern recognition isn’t assumed.

The book is suitable for courses on Machine Learning, computer vision, signal processing, data mining, and bioinformatics. Pattern Recognition and Machine Learning go through all basic algorithms with statistics revision. Bayesian methods have been greatly enhanced by the development i.e. as variational Bayes and expectation propagation. While a new kernel-based method has a vital impact on both algorithms and applications.

Topics covered:

1. Approximate inference algorithms
2. Bayesian methods
3. Introduction to basic probability theory
4. Pattern recognition and machine learning
5. Kernel-based methods

Hands on Machine Learning with Scikit-Learn and TensorFlow

Written By: Aurelien Geron

Deep learning gave a quick boost to machine learning. First and foremost, the book requires to have some experience with programming. The deep learning book covers various concepts, techniques, and tools to develop smart systems. 2^nd editions have Keras to its content beside Scikit-Learn and TensorFlow. After careful reading one can implement an intelligent supervised learning system. This also explores several training models, including support vector machines, decision trees, random forests, and ensemble methods.

Topics covered

1. Deep neural networks
2. Reinforcement learning
3. Explore various training models.
4. Linear regression
5. Training a neural network
6. Decision trees & Ensemble methods
7. Random forests & Support vector machines
8. Convolutional neural networks, recurrent networks, and deep reinforcement learning
9. Learning techniques for training

After covering, basic to mediatory level, it’s good to jump to reading scientific papers. The latest papers provide an opportunity for what has already implemented and what they can implement. Usually, they keep us up to date with the latest technology. At the expert level, reading ai books might not prove to be fruitful.

Machine Learning: A Probabilistic Perspective

Written by : Kevin P. Murphy

A comprehensive introduction to machine learning uses probabilistic models and implications as a unifying approach. Machine learning develops methods to automatically detect patterns in data. This book is written in pseudo-code for vital algorithms with worked examples.

Mathematics lovers will surely love this book. It’s a masterpiece of the mathematics behind all machine learning methods. The book uses graphical models to specify them concisely. Most of the discussed models are already implemented in MATLAB and PMTK (probabilistic modeling toolkit). Suitable for mediators to advance carrier in machine learning and show it as a reference in machine learning research.

Topics covered:

1. Unified and Probabilistic approach
2. Probability
3. Linear algebra
4. Conditional random fields
5. L1 regularization
6. Deep learning
7. Optimization

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Data Science Job: How to Become a Data Scientist Paperback

Written By: Przemek Chojecki

One of the hottest jobs in the market is data scientist. A great book by Przemek Chojecki covers all the concepts of How to become a data scientist. The book will guide you step by step in the journey of becoming a data scientist. Trending Jobs in the market have chances to grow rich. To get started in that field its good practice to get knowledge of working with multiple companies as a project manager, a data science consultant, or a CTO. Finally, to land your carrier in that field, its crucial to learn some to-dos to keep on track.

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Some Other Best Machine Learning Books

1. A Brief Introduction to Neural Networks by David Kriesel
2. A Programmer’s Guide to Data Mining by Ron Zacharski
3. Fundamentals of Deep Learning: Designing Next-Generation Machine Intelligence Algorithms by Nicholas Locascio and Nikhil Buduma
4. Machine Learning: The Art and Science of Algorithms that Make Sense of Data by Peter A. Flach
5. Machine Learning with R: Expert Techniques for Predictive Modeling by Brett Lantz
6. Massive Datasets Mining  by Jeffrey David Ullman
7. Neural Networks and Deep Learning by Pat Nakamoto
8. The Elements of Statistical Learning: Data Mining, Inference, and Prediction by Jerome H. Friedman, Robert Tibshirani, and Trevor Hastie
9. Probability, and Statistics for Programmers by Allan B. Downey
10. Probabilistic Graphical Models Techniques by Nir Friedman

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Conclusion:

To sum up the best machine learning books, it’s hard to choose which one works best for you. Knowing all these concepts is crucial for anyone who is seeing a bright carrier in that field. Yet, this field is now at its boom. Furthermore, choosing the best book depends on your intent. If you are looking for pattern recognition then might be some great ai books not work well. In short, if you found any trouble or as a newbie wants to grow then official tutorials, GitHub, and stack overflow are the best sources to help in any problem.