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Best Machine Learning Books

Best Machine Learning Books

Machine Learning is one of the most sought-after technologies of today. With its wide applications and techniques, it already has disrupted many industries and is poised to impact more in the future. From healthcare to manufacturing, nearly all industries are transforming with its incorporation, becoming more optimized and efficient as we speak. Regardless to say, graduates and professionals should seriously consider upskilling to roles in these fields if they want to stay relevant in future.

However, making a career in this lucrative domain is not so easy. Though there is no dearth of learning materials, courses and certifications in the domain, beginners will find it especially difficult to get started. Mostly because there is a lot of information out there, which requires them to weigh one against another to finally decide upon the most valuable ones. It can consume a lot of effort and time.

To save them from this, we have identified the best machine learning books that can help them to build their basics and kick start their career in the domain.

Top 10 Machine Learning Books For Young Graduates & Beginners

1. Machine Learning by Tom M Mitchel

Hailed as the’ bible of Machine Learning, this book by Mitchell is a must-have for young graduates and professionals entering into the Machine Learning domain. It provides exhaustive coverage of all the fundamental concepts and algorithms, like neural networks and Bayesian learning at a high level. The book also contains a lot of examples and case studies of machine learning algorithms. This will help readers to easily grasp complex theories and form a solid base which they can use as a launchpad to do further in-depth research.

Some of the key topics covered are:

– Primary approaches to ML
– Machine Learning concepts and techniques
– Genetic Algorithms
– Reinforcement Learning
– Inductive Logic programming

2. Machine Learning for Absolute Beginners by Oliver Theobald

This is ideal for those who neither knows to code nor has any background in mathematics. It explores all fundamentals from scratch such as downloading free datasets to libraries that they need. Everything is explained in a clear, concise manner in plain old english. Machine learning cannot get simpler than this!

Some of the key concepts covered are:
– Basics of Neural Networks
– Data Scrubbing
– Decision Trees
– Regression Analysis
– Clustering

3. Introduction To Machine Learning with Python by Andreas C Muller & Sarah Guido

This book is perfect for absolute beginners as it can be easily consumed even by those who are not well-versed in the programming language Python. Through easy examples, the book explores the fundamental machine learning concepts and algorithms. There is more importance placed on the practical aspects of ML than the math behind it. By the end, readers will be able to learn various ways of building their own practical machine learning solutions.

Some of the key topics covered are:
– Fundamentals of machine learning
– Top ways to improve data science and machine learning skills
– Pros and Cons of various machine learning algorithms
– Advanced techniques for evaluating models and tuning parameters
– Ways to represent data processed by machine learning
– Techniques for working with text data
– Exploring pipelines for Model chaining

4. The Hundred-Page Machine Learning Book by Andriy Burkov

This succinct introduction to machine learning is well-endorsed by industry thought leaders from Google, eBay, and many others. In a mere 100 pages, it manages to crisply cover both theory and practice. This book is a practical guidebook that can help the reader to start their machine learning projects in days.

Some of the key concepts covered are:
– Essential Algorithms in Machine Learning
– Analysis of Learning Algorithm
– Exploring Neural Networks and Deep Learning
– Different Learning approaches – Supervised, Unsupervised, and other forms of learning

5. Hands-On Machine Learning with Scikit-Learn and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems by Aurélien Géron

This book is ideal for those who have good knowledge of Python. It will help them to gain an intuitive understanding of machine learning concepts and tools that are essential to building intelligent systems. Through concrete examples, it explores two production ready Python frameworks, Scikit-learn and TensorFlow frameworks. Post-reading this book, they will be able to build smart intelligent systems that are capable of learning from data.

Some of the key concepts covered are:
– Understanding machine learning landscape, especially neural nets
– Deploying scit-learn to track machine learning projects end to end
– Comprehensive understanding of training models, including decision trees, support vector machines, ensemble methods and random forests
– Exploring neural net architecture and learning to train and scale neural nets
– Deploying TensorFlow library to build and train neural nets

6. Python Machine Learning by Sebastian Raschka and Vahid Mirjalili

This is a language specific introduction to machine learning, perfect for those who want to dive into programming. It starts easy, with actual examples of code and progresses to recent advancements in machine learning and deep learning. Readers can learn the implementation of various machine learning algorithms, especially scikit-learn.

Some of the key concepts covered are
– Important frameworks in Machine Learning, Deep Learning and Data Science
– Using Python’s open-source libraries for machine learning
– Using TensorFlow to implement deep neural networks
– Using sentiment analysis, clustering and regression analysis to perform various activities

7. Artificial Intelligence: A Modern Approach by Stuart J. Russell and Peter Norvig

This book is included in the curriculum of various university-level AI programs. It contains a thorough introduction into machine learning and covers all the jargons in a concise manner with clear examples. This is perfect for all beginners to get started in the domain and familiarize themselves with key research topics.

Some of the key concepts covered are:
– Artificial Intelligence for modern applications
– Logical Reasoning Systems
– Reinforcement Learning
– Learning with Neural Networks
– Machine translation, Robotics and Speech recognition

8. Pattern Recognition and Machine Learning By Christopher M. Bishop

This has been recommended by universities since it was first published in 2006. It explores the theoretical aspects of machine learning, including the statistical techniques behind it. It’s ideal for those who have a solid base in advanced mathematical concept, like linear algebra, multivariate calculus and basic understanding of probabilities

Some of the key topics covered are:
– Bayesian perspective on Machine Learning
– Graphical models for defining and applying probabilistic models
– Mixture Models
– Approximate inference

9. The Elements of Statistical Learning: Data Mining, Inference, and Prediction By Trevor Hastie, Robert Tibshirani, and Jerome Friedman

This book is comprehensive coverage of several topics, such as neural networks, classification trees, random forest boosting, support vector machines, and many more. Readers can use it as a reference book for deepening their understanding in Artificial Intelligence and Machine Learning. Graduates from Statistics will find the book particularly enjoyable.

Some of the key topics covered are:

– Supervised and Unsupervised Learning
– Graphical Models
– Spectral clustering
– Path algorithms and least angle regression for the lasso
– Non-negative matrix factorization
– Wide data

10. Artificial Intelligence: 101 Things You Must Know Today About Our Future by Lasse Rouhiainen

This book explores the future implications of AI. It presents well-researched real world-examples of how technological breakthroughs in AI have affected industries such as healthcare, marketing, tourism, retail, agriculture finance, sales and education. Other interesting topics touched upon are robots, self-driving cars, and chatbots

Some of the key topics discussed are:
– AI and its impact on Business Processes
– AI and its impact on Industries
– AI and its impact on the Job Market

Other than these books, machine learning beginners can also check out the following books to deepen their understanding of the domain:

  • Applied Predictive Modeling by Max Kuhn and Kjell Johnson Make Your Own Neural Network by Tariq Rashid

  • Mathematics for Machine Learning by Marc Peter Deisenroth

  • Machine Learning: A Probabilistic Perspective (Adaptive Computation and Machine Learning series) by Kevin P Murphy

  • Data Science from Scratch: First Principles with Python by Joel Grus

Conclusion

Books are a great way for newbie learners to start their self-paced learning in complex technologies such as Artificial Intelligence, Machine Learning and Data Science. That is because it provides knowledge in an organized manner, laying the solid foundation for both theory and practice. As you know, theoretical knowledge is invaluable when one is starting out in a field. Post-reading one or two of these books, they will be better equipped to explore other relevant courses and certifications.

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