• About Me
  • Contact Us
  • Privacy Policy
  • Terms and Conditions
  • Advertise / Sponsor

DOTNET DETAIL

Learn Microsoft .NET Technologies

  • Home
  • Tutorials
    • Angular
      • Angular 5
      • Angular 6
    • ASP.NET Core
    • Azure
    • React
    • Vue
  • Books
  • Courses
  • Cloud Hosting
  • Interview Questions
You are here: Home / Books / 20 Best Machine Learning Books for Beginners and Experts

20 Best Machine Learning Books for Beginners and Experts

January 30, 2021 by mebakar1005 Leave a Comment

In the era of Information Technology and Artificial Intelligence machine learning has become a tool to see the world and process visual data. Machine learning has taken the position of the most in-demand job in today’s world and a machine learning engineer nowadays is earning a handsome salary.

Machine learning algorithms are making computers smarter day by day. With the help of these algorithms computers, today are playing chess, performing surgeries, driving cars, etc. If someone has basic computer knowledge and wants to change careers for a better future, machine learning is a good option.

For that matter, if you are just a machine learning enthusiast and have the curiosity to understand machine learning the world of computer algorithms can be an enchanting and rewarding passion.

We have selected the best top 20 machine learning books catering to beginners or adding a book to the library of an advanced data science learner.

Note:- If you are looking for Best Machine Learning Courses then click here.

1- Artificial Intelligence for Dummies.

Author:                 John Paul Mueller and Luca Massaron
Format:                Kindle Edition
Print length:       316
Publisher:            For dummies
Edition:                 1st

This book helps you not only get a clear picture of technology but also addresses some major myths about it. It gives a very engrossing perspective about the application of technology in everyday life be it, self-driven cars, or its achievements in the field of medicine.

Topic covered:

  1. Discover the history of Artificial Intelligence.
  2. Understanding the role of data.
  3. Role of Artificial Intelligence in computer applications, Medicines, Machine Learning, etc.
  4. Clearing the misconceptions about AI.
  5. Exploration about drones and robots.

Author’s Profile

John Paul Mueller is the author of 108 books and more than 600 articles covering topics like AI, Networking, and Data Base Management. He is a technical editor and consultant by profession.

Luca Massaron is a specialist in multivariate statistical analysis, machine learning, and customer insight. Professionally he is a data scientist and marketing, research director.

Buy Now

2- Artificial Intelligence a Modern Approach.

Author:                Russel
Format:                Kindle
Print Length:      1136
Publisher:            Pearson
Edition:                 4th

Around the world, many faculties of different universities recommend this book to students who are beginners and are just entering the world of artificial intelligence. This book gives a detailed insight into the field of AI and related research topics. This book also provides helpful references for further study. As it is a very detailed book that is why it cannot be read quickly especially when we want to have a sound command on the topic.

Topics Covered:

  1. Introduction
  2. Intelligent Agents
  3. Searching to solve problems
  4. Knowledge and Reasoning
  5. Uncertain knowledge and reasoning
  6. Learning
  7. Communicating, Perceiving, and Acting
  8. Conclusions

Author’s Profile

Stuart Russel is a professor of computer science at the University of California, Berkeley, and adjunct professor of neurological surgery at the University of California, San Francisco. He is a computer scientist and 1400 universities in 128 countries recommend his book Artificial Intelligence a Modern Approach.

Buy Now

3- Life 3.0: Being Human in the Age of Artificial Intelligence.

Author:                Max Tegmark
Format:                Kindle Edition
Print Length:      384
Publisher:            Penguin
Edition:                 1st

This book encompasses the tremendous development in the field of artificial intelligence and its potential to turnaround the future of mankind more than any other form of technology. This book also discusses the point of view on some controversial topics like consciousness and eventual physical limits on life in the solar system.

Topics Covered:

Life 1.0 (Biological stage): Evolving its hardware and software.

Life 2.0 (Cultural stage): Evolving its hardware and designing most of its software.

Life 3.0 (Technical stage): Designing its hardware and software.

Authors Profile

An MIT professor Max Tegmark is the author of two books and more than 200 technical papers. The topics range from Cosmology to Artificial Intelligence. His unorthodox ideas and love for adventure has got him the name of “Mad Max”

Buy Now

4- Machine Learning

Author:                Tom M. Mitchell
Format:                Textbook
Publisher:            McGraw Hill Education
Print length:       432
Edition:                 1st

This book is a detailed study of Machine learning algorithms and theorems. It comprises detailed examples with case studies to help the reader in having precise knowledge about machine learning algorithms. Anyone aiming to start a career in machine learning, this book will prove to be the ultimate guide for a beginner.

See also  Best Machine Learning Courses Available Online | Both Paid & Free in 2021

Topics covered:

  1. Genetic Algorithms.
  2. Inductive logic programming.
  3. Introduction to primary approaches regarding machine language.
  4. Concepts and techniques of Machin learning.
  5. Re-enforcement learning.

Author’s profile

Tom M. Mitchell is a university professor at Carnegie Mellon University. His contribution to the advancement of machine learning, artificial intelligence, and cognitive neuroscience is phenomenal.

Buy Now

5- Deep learning by Goodfellow Et. Al

Author:                 Ian Goodfellow, Yoshua Bengio, and Aaron Courville
Format:                Kindle
Publisher:            MIT press
Pages:                   800
Edition:                 1st

According to Elon Musk, Deep Learning is a comprehensive and complete book on the topic. From the start of the decade, deep learning has become a steppingstone in the world of technology. This book has the basic concepts, practical aspects, and topics related to advanced research which make it a hands-on guidebook not only for the learners and practitioner but instructors as well.

Topics Covered:

  1. Introduction.
  2. Part I:    Applied Math and Machine Learning Basic.
  3. Part II:   Modern Practical Deep Network
  4. Part III: Deep Learning Research

Authors’ Profile

Ian Goodfellow is serving as a research scientist at Google.

Yoshua Bengio, at the Université de Montréal is a professor of computer science.

Aaron Courville, at the Université de Montréal is an assistant professor of computer science.

Buy Now

6- Data Science from Scratch: First Principles with Python

Author:                 Joel Grus
Format: Kindle Edition/Paperback
Publisher:            O’ Reilly
Pages:   500
Edition: 2nd

Math and science are the core of data science. This book will guide you to learn them as well as hacking skills which are very much required to be a data scientist. This book also helps you to explore the natural processing of language and the analysis of the network.

Topics Covered:

  1. Implement k-nearest neighbors.
  2. Naïve Bayes.
  3. Linear and Logistic Regression.
  4. Decision Trees.
  5. Clustering Models.

Author’s Profile

Joel Grus is a software engineer by profession and is working at google.com. He has experience working as a data scientist at various startups. He is a regular in attending data science happy hours.

Buy Now

7- Pattern Recognition and Machine Learning

Author:                Christopher M. Bishop
Format:                Hardcover/Kindle/Paperback
Publisher:            Springer
Pages:                   738
Edition:                 2nd

This book is the first of its kind which gives a graphical model on machine learning. This book provides comparative inference algorithms that give quick answers to situations where clear-cut answers are not possible. To be able to grasp all the concepts presented in the book you need to have a basic, understanding of Multivariate Calculus and Basic Linear Algebra.

Topics Covered

  1. Approximate inference algorithms.
  2. Bayesian Methods.
  3. Introduction to basic probability theory.
  4. Introduction to pattern recognition and Machine learning.
  5. New Models based on Kernels.

Author’s Profile

Chris Bishop is a Microsoft scientist and Laboratory Director at Microsoft Research Cambridge. He is also serving as a professor of computer science at the University Of Edinburgh and a Fellow of Darwin College Cambridge.

Buy Now

8- Machine Learning for Absolute Beginners: A Plain English Introduction.

Author:                Oliver Theobald
Format:                Kindle/Paperback
Publisher:            Scatterplot Press
Pages:                   164
Edition:                 2nd

If you are looking for a book that is neither long nor has complex language, then this book is a must-read for you. Although plain English is used this book covers all the topics related to high-level introduction to Machine Learning in a practical and beginner-friendly way.

Topics Covered:

  1. Introduction
  2. What is Machine Learning
  3. ML Categories?
  4. The ML Toolbox
  5. Data Scrubbing
  6. Setting Up Your Data
  7. Regression Analysis
  8. Clustering
  9. Bias & Variance
  10. Artificial Neural Networks
  11. Decision Trees
  12. Ensemble Modeling
  13. Building A Model In Python
  14. Model Optimization
  15. Further Resources
  16. Downloading Datasets
  17. Final Word

Author’s Profile

Oliver Theobald has enjoyed Best Seller Status on Amazon. His book Machine Learning for Absolute Beginners has been adopted by many universities. He has a background in technical writing/documentation and operations using AI and cloud computing. Recently he is into BI (Business Intelligence).

Buy Now

9- Make Your Own Neural Network

Author:                 Tariq Rasheed
Format:                Kindle/Paperback
Publisher:            Create space for independent publishing
Pages:                   222
Edition:                 2nd

Deep Learning and artificial intelligence both have a key element of neural networks. This guidebook gives you an understanding of neural networks in a simple yet insightful manner. Simple knowledge of secondary school mathematics will make the understanding of neural networks easy and coding in python become accessible.

See also  10 Best Machine Learning Laptops

Topics Covered:

  1. Introduction to mathematical ideas underlining the neural networks.
  2. Python programming language and neural network buildup.
  3. Performance of neural networks and get is all working on a Raspberry P

Author’s Profile

The author is a physics degree holder with a Master’s in Machine Learning and Data Mining. London Python meet-up group is lead by him.

Buy Now

10- Python Machine learning: A technical approach to Machine Learning for Beginners.

Author:                 Leonard Edison
Format:                Paperback
Publisher:            Create space independent publishing platform
Pages:                   292
Edition:                 1st

After completing this book, you will be equipped to use python for writing simple codes. It will also direct you in the right direction. Once you have gone beyond the beginner level in python.

Topics Covered:

  1. Basics of Artificial Intelligence.
  2. Some of the branches of artificial intelligence.
  3. Decision Trees.
  4. Basic Python programming language.
  5. Logistic Regression

Author’s Profile

Leonard Edison is a computer science teacher and who writes blogs as well. For the past some years he is using his experience in this field to write books to pass on his knowledge to the readers.

Buy Now

11- Information Theory, Inference and Learning Algorithms

Authors:              David J. C. MacKay
Format:                Kindle/Hard Cover/Paperback
Publisher:            Cambridge University Press
Pages:                   640
Edition:                 1st

This book was published almost 20 years ago but its relevance cannot be denied even today. It has a multi-disciplinary approach to establish connections between information theory learning algorithms and inference. It does not give its reader a lot of practical examples, but it serves its purpose as an introductory book for beginners.

Topics Covered:

  1. Data Compression
  2. Noisy-channel coding
  3. Further Topic in Information Theory
  4. Probabilities and Inference
  5. Neural Networks
  6. Sparse Graph Codes

Author’s Profile

David J. C. MacKay was the Regius professor of engineering in the department of engineering at the University of Cambridge. He also served as Chief Scientific Advisor at the Department of Energy and Climate Change in the UK.

Buy Now

12-  Architect of Intelligence for Humans: Volume 1

Author:                Jeff Heaton
Format:                Kindle/Paperback
Publisher:            CreateSpace Independent Publishing Platform
Pages:                   224                       
Edition:                 1st

In this book, algorithms are explained with the help of actual numeric calculations which can be performed by the reader himself. This is specially designed to cater to those people who learn AI but do not have a thorough mathematical knowledge.

Topics Covered:

  1. Basic Algorithms of Artificial Intelligence
  2. Dimensionality
  3. Distance Metrics
  4. Clustering
  5. Error Calculation
  6. Hill Climbing
  7. Nelder Mead
  8. Linear Regression

Author’s Profile

Jeff Heaton is professionally a computer scientist with a specialization in Python, R, Java, and C#. Jeff has a master’s degree in Information Management and Ph.D. in Computer Science. He has authored more than 10 books.

Buy Now

13-  Neural Networks and Deep Learning

Author:                 Charu C. Aggarwal
Format:                E-Textbook/Hard Cover/Paperback
Publisher:            Springer
Pages:                   520
Edition:                 1st

This book explains deep learning with classical and modern models. Neural Networks, their theory, and algorithms have been discussed in this book in great detail. This book explains Machine Learning through Neural Networks and the theory behind them.

Topics Covered:

  1. The Basics of Neural Networks
  2. Fundamentals of Neural Networks
  3. Advanced Topics in Neural Networks

Author’s Profile

Charu C. Aggarwal is a DRSM (Distinguished Research Staff Member) at IBM at the Watson Research Center in Yorktown Height, NY. In 1993 he acquired his undergraduate degree in Computer Science from IIT at Kanpur and his Ph.D. from MIT in 1996. He has exhaustive experience in the Data Mining field.

Buy Now

14-  Hands-on Machine Learning

Author:                 Aurélien Géron
Format:                Kindle/Paperback
Publisher:            O’Reilly
Pages:                   600
Edition:                 2nd

Deep intuitive understanding regarding the concepts and rules to build intelligent systems can be learned through this book. The author has used two production-ready Python frameworks that are TensorFlow and Scikit-Learn. With thorough examples but minimal theory to impart the knowledge of deep learning.

See also  Top 10 Programming Languages of the Future.

Topics Covered:

  1. Fundamentals of Machine Learning
    1. The Machine Learning Landscape
    1. End-to-End Machine Learning Project
    1. Classification
    1. Training Models
    1. Support Vector Machines
    1. Decision Trees
    1. Ensemble Learning and Random Forests
    1. Dimensionality Reduction
  2. Neural Networks Deep Learning
    1. Up and Running with TensorFlow
    1. Introduction to Artificial Neural Networks
    1. Training Deep Neural Nets
    1. Distributing TensorFlow Across Devices and Servers
    1. Convolutional Neural Networks
    1. Recurrent Neural Networks
    1. Autoencoders
    1. Reinforcement learning

Author’s Profile

Aurélien Géron is a consultant for machine learning. He is a former Googler and leader of YouTube’s video classification team. He has worked in different domains like Finance, Defense, and Health Care as a software engineer.

Buy Now

15-  Generative Deep Learning: Teaching Machines to Paint, Write, Compose and Play

Author:                David Foster
Format:                Kindle/Paperback
Publisher:            O’Reilly Media
Pages:                   330
Edition:                 1st

Artificial Intelligence has made it possible that a machine can be taught to paint, write, and compose music. This book will teach machine learning engineers and data scientists to recreate generative deep learning models like auto-encoders, encoder-decoder models, world models, etc.

Topics Covered:

  1. Generative Modelling
  2. Deep Learning
  3. Variational Autoencoders
  4. Generative Adversarial Networks
  5. Paint
  6. Write
  7. Compose
  8. Play
  9. Future of Generative Modelling
  10. Conclusion

Author’s Profile

David Foster is the co-founder of a data science consultancy named Applied Data Science. He is a winner of different international machine learning competitions and has won first prize for visualization to optimize site selection for the sake of clinical trials for a pharmaceutical company in the US. He is a master’s in mathematics and Operational Research from Trinity College, Cambridge, and the University of Warwick respectively.

Buy Now

16-  Python Machine Learning: A Technical Approach to Machine Learning for Beginners

Author:                Leonard Eddison
Format:                Audio Book/Paperback
Publisher:            CreateSpace Independent Publishing Platform
Pages:                   292
Edition:                 1st

This book is designed especially for beginners and encapsulates the basics and importance of machine learning. It also focuses on different branches of machine learning and their applications in a wider spectrum. This book enables its readers’ coding in Python.

Topics Covered:

  1. Basics of AI
  2. Decision Trees
  3. Deep Neural Networks
  4. Basics of Python Programming Language
  5. Logistic Regression

Author’s Profile

Leonard Eddison is a blogger and teacher of Computer Science. He has written many books. He was born in Buffalo, NY. He passionately writes books to transfer his knowledge to them to pass it on to others.

Buy Now

17-  Python Machine Learning: Unlock Deeper Insights into ML

Author:                Sebastian Raschka
Format:                Kindle/Paperback
Publisher:            Ingram Short Title
Pages:                   454
Edition:                 1st

Readers can access the world of predictive analysis with the help of this book. It teaches the practices and methods for the improvisation and optimization of machine learning systems and algorithms.

Topics Covered:

  1. Giving Computers the Ability to Learn from Data
  2. Training Machine Learning Algorithms for Classification
  3. A Tour of Machine Learning Classifiers Using Scikit-Learn
  4. Building Good Training Sets – Data Processing
  5. Compressing Data via Dimensionality Reduction
  6. Learn Best Practices for Model Evaluation and Hyperparameter Tuning
  7. Combining Different Models for Ensemble Learning
  8. Applying Machine Learning to Sentiment Analysis
  9. Embedding ML Model into a web application
  10. Predicting Continuous Target Variables with Regression Analysis
  11. Working with Unlabeled Data – Clustering Analysis
  12. Training Artificial Neural Networks for Image Recognition
  13. Parallelizing Neural Network Training with Theano

Author’s Profile

Sebastian Raschka is a student at Michigan State University, Pursuing his Ph.D. He has been ranked by GitHub as the most influential data scientist. He is regularly contributing to the methods he implemented to open-source projects.

Buy Now

18-  Data Mining

Author:                 Ian H. Witten, Eibe Frank, Mark A. Hall
Format:                Kindle/Paperback
Publisher:            Morgan Kaufmann
Pages:                   654
Edition:                 4th

This book gives a thorough knowledge of the concepts of ML and the application of tools and techniques in the mining situation of real-world data.

Topics Covered:

  1. Clustering
  2. Comparing Data Mining Methods
  3. Knowledge representation & Clusters
  4. Linear Models
  5. Predicting performance
  6. Statistical Modelling
  7. Traditional and Modern data mining techniques

Authors’ Profile

Ian H. Witten is a Chartered Engineer at the Institute of Electrical Engineers London. He is a computer scientist at Waikato University, New Zealand. Eibe Frank is a computer scientist and developer of the WEKA machine. Mark Hall is a Data Scientist at Pyramid Analytics

Buy Now

19-  Machine Learning with TensorFlow

Author:                Nishant Shukla
Format:                Paperback/E-book
Publisher:            Manning Publications
Pages:                   272
Edition:                 1st

This book describes the ML basics with clustering, prediction algorithms, and traditional classification. Its deep learning concepts makes the reader qualified for ML task by using open-source, free TensorFlow library.

Topics Covered:

  1. Autoencoders
  2. Convolutional, recurrent, reinforcement neural networks
  3. Deep learning
  4. Hidden Markov models
  5. Linear regression
  6. Reinforcement learning

Author’s Profile

Nishant Shukla is a researcher in computer vision models focusing on ML techniques in robotics.

Buy Now

20-  Introduction to Machine Learning with Python: A Guide for Data Scientists

Author:                Andreas C. Muller, Sarah Guido
Format:                Kindle/PaperBack
Publisher:            O’Reilly Media
Pages:                   392
Edition:                 1st

This book teaches practical methods of building ML solutions. It teaches important steps for constructing ML applications using Scikit-Learn and Python.

Topics Covered:

  1. Advanced methods for model evaluation and parameter tuning
  2. Applications, fundamental concepts of ML
  3. ML algorithms
  4. Methods for working with text data
  5. Pipelines for chaining models and encapsulating workflow
  6. Representation of processed data.

Author’s Profile

Andreas C. Muller acquired his Ph.D. in ML from the University of Bonn. He worked in Amazon as an ML researcher on computer vision applications. Sarah Guido works at Reonomy as a data scientist.

Buy Now

Conclusion

We have compiled a detailed and extensive list of Machine Learning books. These will deliver detailed information on ML for beginners as well as experts. There are other resources also available to expand one’s knowledge of ML. These books tell us that ML is the way forward for IT novices and experts.

List of some other machine learning books

  • Advances in Financial Machine Learning ( by Marcos Lopez de Prado )
  • Machine Learning: An Algorithmic Perspective (by Stephen Marsland)
  • Deep Learning (Adaptive Computation and Machine Learning series)
  • Think Stats – Probability, and Statistics for Programmers by Allan B. Downey
  • Neural Networks and Deep Learning (by Pat Nakamoto)
  • The Hundred-Page Machine Learning Book
  • AI and Machine Learning for Coders: A Programmer’s Guide to Artificial Intelligence
  • Machine Learning Design Patterns: Solutions to Common Challenges in Data Preparation, Model Building, and MLOps
  • Mathematics for Machine Learning
Best Machine Learning Courses Available Online | Both Paid & Free in 2021
10 Best Machine Learning Laptops

Related

Filed Under: Books, Machine Learning Tagged With: Books, Machine Learning, Machine Learning Books

Leave a Reply Cancel reply

Your email address will not be published. Required fields are marked *

Buy me a coffeeBuy me a coffee

Jobs

Dotnet Jobs

Join Us

Join Us

Subscribe to Blog via Email

Enter your email address to subscribe.

Recent Posts

  • Top LINQ interview questions and answers.
  • Difference between .NET Core and .NET Framework
  • Top 10 Programming Languages of the Future.
  • Top MVC Interview Questions & Answers
  • 20 best Data Science Books: Beginner to Advanced Level
  • 10 Best Machine Learning Laptops
Copyright © 2022