Best Books for Machine Learning 2021

I know, you are plentiful excited to know about Best Books for Machine Learning and would love to make robots alike with their own intelligent brain or else make it capable to recognize different faces, make it capable to stroll around or use those heaps of data (user logs, e-commerce data, financial data, production data, sensors data, hotline stats, HR reports) for meaningful insights. The Best Books for Machine Learning would help you achieve the following:
• Segment customers based on age, geography, gender, buying habits, product selection and find the best marketing strategy for each group
• Recommend products for each client based on what they previously bought or what similar group of clients bought
• Detect which transactions are likely to be fraudulent in online payments
• Forecast next year’s revenue from the data available recently
• And much more
Here are the recommended Best Books for Machine Learning that you can study for. This list for Best Books for Machine Learning has been prepared based on the contents of the books, user ratings and comments on Amazon and their sold volume and their approach in tackling real-world challenges.

Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems

Author: Aurélien Géron

This book, best book for Machine Learning for Beginners, written by the author Aurélien Géron assumes that you know nothing or very little about Machine Learning. It provides you enough concepts, tools, and insights that are required to implement programs capable of learning from data at the beginners level. This book, Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems covers almost all the techniques, from the simplest and most commonly used such as linear regression to some of the advanced Deep Learning techniques that are at next level. The exercises provided in each chapter will help you to apply what you have learned across the chapter and with some programming experience you can dive deep.
• Explore the machine learning landscape, particularly neural nets
• Use Scikit-Learn to track an example machine-learning project end-to-end
• Explore several training models, including support vector machines, decision trees, random forests, and ensemble methods
• Use the TensorFlow library to build and train neural nets
• Dive into neural net architectures, including convolutional nets, recurrent nets, and deep reinforcement learning
• Learn techniques for training and scaling deep neural nets


This book will guide you through the following production-ready python frameworks:
Scikit-Learn is open source and very easy to use, built in NumPy, SciPy, and matplotlib; yet it implements many Machine Learning algorithms efficiently for predictive data analysis, so it makes for a great entry point to learn Machine Learning.
TensorFlow is a free and open-source software library, since November 2015, with version 2.0 releasing in October 2019 for machine learning that can be used in various tasks but has a particular focus on training and inference of deep neural networks. TensorFlow is a more complex library for distributed numerical computation. It makes it possible to train & run very large neural networks efficiently by distributing the computations across potentially hundreds of multi-GPU servers. TensorFlow was created at Google and supports many of its large-scale applications.
Keras is an open-source software library that provides high-level Deep Learning API that makes it easy and simple to train and run artificial neural networks. Keras acts as an interface for the TensorFlow library. Up until version 2.3, Keras supported multiple backends, including TensorFlow, Microsoft Cognitive Toolkit (CNTK), R, Theano, and PlaidML. As of version 2.4 Keras, only TensorFlow is supported.

The topics covered in this book are:
Part I: The fundamentals of Machine Learning

  1. The Machine Learning Landscape
  2. End-to-End Machine Learning Project
  3. Classification
  4. Training Models
  5. Support Vector Machines
  6. Decision Trees
  7. Ensemble Learning and Random Forests
  8. Dimensionality Reduction
  9. Unsupervised Learning Techniques

Part II: Neural Netorks and Deep Learning

  1. Introduction to Artificial Neural Networks with Keras
  2. Training Deep Neural Networks
  3. Custom Models and Training with TensorFlow
  4. Loading and Preprocessing Data with TensorFlow
  5. Deep Computer Vision Using Convolutional Neural Networks
  6. Processing Sequences Using RNNs and CNNs
  7. Natural Language Processing with RNNs and Attention
  8. Representation Learning and Generative Learning Using Autoencoders and GANs
  9. Reinforcement Learning
  10. Training and Deploying TensorFlow Models at Scale
    A. Exerise Solutions
    B. Machine Learning Project Checklist
    C. SVM Dual Problem
    D. Autodiff
    E. Other Popular ANN Architectures
    F. Special Data Structures
    G. TensorFlow Graphs

Superintelligence: Paths, Dangers, Strategies

Author: Nick Bostrom

Superintelligence: Paths, Dangers, Strategies

The New York Times bestseller, Superintelligence: Paths, Dangers, Strategies by Nick Bostrom is another Best Book for Machine Learning where the writer makes an understanding for the future of humanity and intelligent life by asking the question, “What happens when machines surpass humans in general intelligence? Will artificial agents save or destroy us?”. Our brain has certain distinctive capabilities that other animals lack which makes us dominant over them. But it may be the case, machine brain surpasses human brain in general intelligence and this super-intelligence could become more powerful that they could go beyond our control. The fate of humankind may depend upon the actions of machine super-intelligence. Some of the reviews about Superintelligence: Paths, Dangers, Strategies; the Best Machine Learning Book.

“I highly recommend this book.” — Bill Gates

“Worth reading. We need to be super careful with AI. Potentially more dangerous than nukes.” — Elon Musk, Founder of SpaceX and Tesla

“This superb analysis by one of the world’s clearest thinkers tackles one of humanity’s greatest challenges: if future superhuman artificial intelligence becomes the biggest event in human history, then how can we ensure that it doesn’t become the last?” — Professor Max Tegmark, MIT

“Every intelligent person should read it.” — Nils Nilsson, Artificial Intelligence Pioneer, Stanford University

The Topics covered in this book are:

  1. Past Development and Present Capabilities
  2. Paths to SuperIntelligence
  3. Forms of SuperIntelligence
  4. The Kinetics of an Intelligent Explosion
  5. Decisive Strategic Advantage
  6. Cognitive Superpowers
  7. The SuperIntelligent Will
  8. Is the Default Outcome Doom?
  9. The Control Problem
  10. Oracles, Genies, Sovereigns, Tools
  11. Multipolar Scenarios
  12. Acquiring Values
  13. Choosing the Criteria for Choosing
  14. The Strategic Picture
  15. Crunch Time
    Partial Glossary

An Introduction to Statistical Learning: with Applications in R (Springer Texts in Statistics)

An Introduction to Statistical Learning: with Applications in R (Springer Texts in Statistics)

Gareth James, Professor of Data Sciences and Operations at the University of Southern California
Daniela Witten, Associate Professor of statistics and biostatistics at the University of Washington
Trevor Hastie and Robert Tibshirani, Professors of statistics at Stanford University

Another Best Book for Machine Learning, An Introduction to Statistical Learning: with Applications in R provides an overview for Statistical Learning, an important toolset for deriving insights from abundant and complex datasets- be it from finance, biology, marketing or physics. This 440 pages book, An Introduction to Statistical Learning: with Applications in R, presents some of the most important modeling and prediction techniques, along with relevant applications. Important topics that are icluded in this book are linear regression, classification, resampling methods, shrinkage approaches, tree-based methods, support vector machines, clustering, and more. To facilitate the use of these statistical learning techniques by beginners in science, industry, and other fields, evey chapters in this book contains an easy and simple tutorial in implementing the analyses and methods presented in R which is an extremely popular open source statistical software platform.

The Topics covered in this book are:

  1. Introduction
  2. Statistical Learning
  3. Linear Regression
  4. Classification
  5. Resampling Methods
  6. Linear Model Selection and Regularization
  7. Moving Beyond Linearity
  8. Tree-Based Methods
  9. Support Vector Machines
  10. Unsupervised Learning

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