Best Machine Learning and Data Science Books 2020

The world’s most valuable resource is now data! That’s how far The Economist went in relaying the value of data as a commodity, claiming it has now even surpassed the traditional monopoly of oil. Indeed and LinkedIn have consistently ranked Machine Learning Engineers and Data Scientists as some of the hottest professions in the last few years and predict that the demand trend will only continue to grow well into the decade.



Learning and mastering machine learning and data science can be overwhelming on the technical side. There are countless books, online courses, and graduate degrees that are offering this knowledge with varying breadth and depth – so, where do you start?

 

Through the rest of the article, we’ll review some of the most popular books for data science and machine learning for various levels of expertise and understanding, both on technical topics as well as non technical ones. The books below are listed in no particular order and cover a large spectrum of the machine learning and data science fields. Some of these books will require familiarity with some coding languages, mathematical concepts or even prior machine learning experience, but we’ll be sure to mention it if that’s the case.


Superintelligence: Paths, Dangers, Strategies


Author(s):

Nick Bostrom

Price Range (USD):

$21.50 (Hardcover) Buy on Amazon.com

Number of Reviews:

909

Average Review Rating (out of 5)

4.1

Categories

Future of AI; Moral Implications of AI; AI Philosophy; AI and Society

Prerequisites:

N/A

Book Abstract:

Superintelligence asks the questions: What happens when machines surpass humans in general intelligence? Will artificial agents save or destroy us? Nick Bostrom lays the foundation for understanding the future of humanity and intelligent life. 

The human brain has some capabilities that the brains of other animals lack. It is to these distinctive capabilities that our species owes its dominant position. Other animals have stronger muscles or sharper claws, but we have cleverer brains. If machine brains one day come to surpass human brains in general intelligence, then this new superintelligence could become very powerful. As the fate of the gorillas now depends more on us humans than on the gorillas themselves, so the fate of our species then would come to depend on the actions of the machine superintelligence. 

But we have one advantage: we get to make the first move. Will it be possible to construct a seed AI or otherwise to engineer initial conditions so as to make an intelligence explosion survivable? How could one achieve a controlled detonation? 

This profoundly ambitious and original book picks its way carefully through a vast tract of forbiddingly difficult intellectual terrain. Yet the writing is so lucid that it somehow makes it all seem easy. 

After an utterly engrossing journey that takes us to the frontiers of thinking about the human condition and the future of intelligent life, we find in Nick Bostrom's work nothing less than a reconceptualization of the essential task of our time.”

 

Nick Bostrom, in this book, artfully leverages his background in computational neuroscience and Artificial Intelligence (AI) with his inherent philosophical thinking to provide an unprecedented analysis of the future with AI. 

Bostrom imagines how we can create an AI far superior than we could have imagined and what risks it entails in terms of the world and societal power dynamics, while delving into how things can potentially go wrong and if superintelligence can replace us as the dominant lifeform on Earth. He talks about steering the future course through uncharted territory and navigating this still unknown terrain. The mere projection of such potentially grim scenarios forces us to begin considering solutions for them and the author looks at several such solutions.

This book is definitely written from a philosophical standpoint and a great insight into the world we are headed towards and the policies, regulations and thought leadership that is required for a harmonious co-existence.  

The book was recommended by Bill Gates and Elon Musk as worth reading.


Deep Learning with Python


Author(s):

Francois Chollet

Price Range (USD):

$30.49-33.71 (Paperback) Buy on Amazon.com

Number of Reviews:

412

Average Review Rating (out of 5)

4.5

Categories

Deep Learning; Keras; Python; Neural Networks

Prerequisites:

Deep Learning Theory Basics (Recommended); Python (Basic)

Book Abstract:

Deep Learning with Python introduces the field of deep learning using the Python language and the powerful Keras library. Written by Keras creator and Google AI researcher François Chollet, this book builds your understanding through intuitive explanations and practical examples.


In this book, Francois Chollet covers practical deep learning implementation with the Keras library, which is a high-level API and considered a much better entry point for coders to deep learning than TensorFlow, for example, based on complexity. The text strikes a relatively good balance between covering basic foundational concepts with topics and considerations for the advanced practitioners and researchers. 

Mathematical concepts are explained using code snippets and the book is well suited for anyone with basic Python coding experience. 

The first part of the book provides a general fundamental understanding and mathematical building blocks of both machine learning and deep learning. However, you will likely need another book for a more in-depth look into the theoretical side of Deep Learning such as “Deep Learning (Adaptive Computation and Machine Learning series)” by Ian, Yoshua and Aaron, which is the next book discussed. 

The second part of the book introduces different practical applications of deep learning networks including Computer vision with convolutional neural networks (CNNs), natural language processing and transactional data (time series) with recurrent neural networks (RNNs) and generating texts and images using variational autoencoders (VANs) as well as generative adversarial networks (GANs). A recurring theme within deep learning is the problem of overfitting and the book addresses a range of solution techniques as well.

Although all the examples are implemented using Keras, the topics are covered in general perspective and knowledge can be used with other similar high-level frameworks with relative ease.

Deep Learning (Adaptive Computation and Machine Learning series)


Author(s):

Ian Goodfellow, Yoshua Bengio, Aaron Courville

Price Range (USD):

$68.50-69.25 (Paperback) Buy on Amazon.com

Number of Reviews:

759

Average Review Rating (out of 5)

4.1

Categories

Deep Learning; Neural Networks; Convolutional Neural Networks (CNN)

Prerequisites:

Machine Learning (Intermediate)

Book Abstract:

Deep learning is a form of machine learning that enables computers to learn from experience and understand the world in terms of a hierarchy of concepts. Because the computer gathers knowledge from experience, there is no need for a human computer operator to formally specify all the knowledge that the computer needs. The hierarchy of concepts allows the computer to learn complicated concepts by building them out of simpler ones; a graph of these hierarchies would be many layers deep. This book introduces a broad range of topics in deep learning. 

The text offers mathematical and conceptual background, covering relevant concepts in linear algebra, probability theory and information theory, numerical computation, and machine learning. It describes deep learning techniques used by practitioners in industry, including deep feedforward networks, regularization, optimization algorithms, convolutional networks, sequence modeling, and practical methodology; and it surveys such applications as natural language processing, speech recognition, computer vision, online recommendation systems, bioinformatics, and video games. Finally, the book offers research perspectives, covering such theoretical topics as linear factor models, autoencoders, representation learning, structured probabilistic models, Monte Carlo methods, the partition function, approximate inference, and deep generative models. 

Deep Learning can be used by undergraduate or graduate students planning careers in either industry or research, and by software engineers who want to begin using deep learning in their products or platforms. A website offers supplementary material for both readers and instructors.


Written by luminaries in the field such as Yoshua Bengio, considered as one of the world’s leading experts in AI and a pioneer in deep learning, this book is a rigorous and up to date reference of deep learning algorithms that is virtually self-contained.

The book has a good balance of mathematics (advanced statistics, linear algebra, numerical optimization), applications, and research topics. The applications section is definitely the meat of the book and the research topics will mostly be of interest to fellow machine learning researchers.

Do note that this is a rather theoretical book and great for readers to gain intuition behind many of the concepts underpinning deep learning techniques taken for granted. Someone without any prior knowledge of machine learning probably would benefit from studying other introductory machine learning texts before tackling this one. Without a solid background, or at least a deep interest, in mathematics and practical hands-on experience with machine learning, this might end up being somewhat of a dense and challenging read. 

The Book of Why: The New Science of Cause and Effect


Author(s):

Judea Pearl, Dana Mackenzie

Price Range (USD):

$13.29 (Paperback)Buy on Amazon.com

Number of Reviews:

343

Average Review Rating (out of 5)

4.3

Categories

AI; Causality; Data Science; Machine Learning

Prerequisites:

N/A

Book Abstract:

Start asking the big questions and learn how the study of causality revolutionized science and the world.

 

Cause and effect: it's at the center of scientific inquiry, and yet for decades scientists had no way of answering simple questions, such as whether smoking causes cancer. In The Book of Why, Judea Pearl and Dana Mackenzie show how Pearl's work on causality has broken through this stalemate, unleashing a revolution in our knowledge of the world. Anyone who wants to understand how science, the human mind, or artificial intelligence works needs The Book of Why.


The Book of Why is about Causality and discusses how it is different than Big Data and Correlation. Judea Pearl, the main author, argues that data and pattern recognition can only get you so far in understanding complex patterns, and that the more crucial piece is building transparent models on how the data was collected and produced in the first place. Pearl expects that leveraging causal reasoning could provide machines with human-level intelligence. 

Terms and concepts that are explored in the book include causality, correlation, mediation, transportability, counterfactuality, among others. Causality, in the context of how Pearl describes it, is around data transparency in understanding why a certain conclusion is reached. He provides many examples along the book on how just data correlation alone fails because of a lack of upstream transparency. The concepts are certainly more on the abstract side and complex with many intermingled ideas and thoughts, but it does caution around relying solely on data to predict future events. 

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


Author(s):

Aurélien Géron

Price Range (USD):

$43.99-48.07 (Paperback) Buy on Amazon.com

Number of Reviews:

416

Average Review Rating (out of 5)

4.7

Categories

Machine Learning; Deep Learning; Deep Neural Networks (DNN); Convolutional Neural Networks (CNN); Recurrent Neural Networks (RNN)

Prerequisites:

Python (Basic – Intermediate); Basic Statistics

Book Abstract:

Through a series of recent breakthroughs, deep learning has boosted the entire field of machine learning. Now, even programmers who know close to nothing about this technology can use simple, efficient tools to implement programs capable of learning from data. This practical book shows you how.

By using concrete examples, minimal theory, and two production-ready Python frameworks—Scikit-Learn and TensorFlow—author Aurélien Géron helps you gain an intuitive understanding of the concepts and tools for building intelligent systems. You’ll learn a range of techniques, starting with simple linear regression and progressing to deep neural networks. With exercises in each chapter to help you apply what you’ve learned, all you need is programming experience to get started.

  • 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


If you are comfortable with coding in Python and are looking for a quick introduction to both classical machine and deep learning techniques in Python from an experienced practitioner, this is a book well suited just for that. Hands-on Machine Learning is a great surface-level introduction to a vast array of machine learning and deep learning models, including their implementation in Scikit-Learn, Keras and Tensorflow (2.0). The book is comprehensive, written in a friendly tone, and contains a large set of excellent exercises, making it a great introduction to the knowledge areas as well as a useful reference text. The book also manages to strike a good balance between covering classical machine learning and deep learning, and the right amount of theory accompanied by references.

AI Superpowers: China, Silicon Valley, and the New World Order


Author(s):

Kai-Fu Lee

Price Range (USD):

$14.28 (Hardcover)Buy on Amazon.com

Number of Reviews:

618

Average Review Rating (out of 5)

4.4

Categories

AI

Prerequisites:

N/A

Book Abstract:

“Dr. Kai-Fu Lee—one of the world’s most respected experts on AI and China—reveals that China has suddenly caught up to the US at an astonishingly rapid and unexpected pace.  

In AI Superpowers, Kai-fu Lee argues powerfully that because of these unprecedented developments in AI, dramatic changes will be happening much sooner than many of us expected. Indeed, as the US-Sino AI competition begins to heat up, Lee urges the US and China to both accept and to embrace the great responsibilities that come with significant technological power. Most experts already say that AI will have a devastating impact on blue-collar jobs. But Lee predicts that Chinese and American AI will have a strong impact on white-collar jobs as well. Is universal basic income the solution? In Lee’s opinion, probably not.  But he provides a clear description of which jobs will be affected and how soon, which jobs can be enhanced with AI, and most importantly, how we can provide solutions to some of the most profound changes in human history that are coming soon.”


This book is a potential eye opener for those of us unfamiliar with the wide-ranging capabilities and imminent impact of AI. Lee contrasts the Chinese’s work in AI with that in the US. While Chinese AI is based on technologies developed in the US, Chinese companies are now developing their own strategic direction. Lee suggests a future which is not only filled with promise, but also fraught with social challenges, making a strong case that AI will soon determine the relative economic power of nations. 

In this book, Lee explains with striking clarity China is quickly becoming the AI superpower, having the perfect combination of: an indomitable entrepreneurial spirit; supportive government policies; well-trained AI scientists; and more data than any other country in the world. 

The book is an apt recommendation to anyone who wants an understanding of the first principles of AI, why it is so powerful and why we should be concerned about it from a political and societal standpoint. However, AI Superpowers doesn’t really delve into the solutions side of things such as law, regulations, and moral responsibilities.  

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


Author(s):

Gareth James, Daniela Witten, Trevor Hastie, Robert Tibshirani

Price Range (USD):

$53.82 (Hardcover) Buy on Amazon.com

Number of Reviews:

550

Average Review Rating (out of 5)

4.7

Categories

Statistics; Machine Learning; R

Prerequisites:

Basic Statistics (preferred)

Book Abstract:

“An Introduction to Statistical Learning provides an accessible overview of the field of statistical learning, an essential toolset for making sense of the vast and complex data sets that have emerged in fields ranging from biology to finance to marketing to astrophysics in the past twenty years. This book presents some of the most important modeling and prediction techniques, along with relevant applications. Topics include linear regression, classification, resampling methods, shrinkage approaches, tree-based methods, support vector machines, clustering, and more. Color graphics and real-world examples are used to illustrate the methods presented. Since the goal of this textbook is to facilitate the use of these statistical learning techniques by practitioners in science, industry, and other fields, each chapter contains a tutorial on implementing the analyses and methods presented in R, an extremely popular open source statistical software platform.

Two of the authors co-wrote The Elements of Statistical Learning (Hastie, Tibshirani and Friedman, 2nd edition 2009), a popular reference book for statistics and machine learning researchers. An Introduction to Statistical Learning covers many of the same topics, but at a level accessible to a much broader audience. This book is targeted at statisticians and non-statisticians alike who wish to use cutting-edge statistical learning techniques to analyze their data. The text assumes only a previous course in linear regression and no knowledge of matrix algebra.”

 

 

An Introduction to Statistical Learningfocusses on all foundational topics systematically, assuming no prior knowledge from the readers, other than very basic statistics. Therefore, if you consider yourself a newbie, this book will help you approach data science easily.

The core statistical ideas of model optimization such as bias-variance tradeoffs are deeply discussed and revisited through multiple example problems. Chapter are like small case studies, developing progressively in complexity, each starting off with a research question, hypothesized models and data descriptions, followed by the data analysis and modelling with the code in R demonstrated with sufficient detail. No prior knowledge of R is required - the included R exercises are particularly helpful for beginners to learn R.

Deep Medicine: How Artificial Intelligence Can Make Healthcare Human Again


Author(s):

Eric Topol

Price Range (USD):

$11.42-14.69 (Hardcover) Buy on Amazon.com

Number of Reviews:

143

Average Review Rating (out of 5)

4.4

Categories

Healthcare; AI

Prerequisites:

N/A

Book Abstract:

“One of America's top doctors reveals how AI will empower physicians and revolutionize patient care Medicine has become inhuman, to disastrous effect. The doctor-patient relationship--the heart of medicine--is broken: doctors are too distracted and overwhelmed to truly connect with their patients, and medical errors and misdiagnoses abound. In Deep Medicine, leading physician Eric Topol reveals how artificial intelligence can help. AI has the potential to transform everything doctors do, from notetaking and medical scans to diagnosis and treatment, greatly cutting down the cost of medicine and reducing human mortality. By freeing physicians from the tasks that interfere with human connection, AI will create space for the real healing that takes place between a doctor who can listen and a patient who needs to be heard.

Innovative, provocative, and hopeful, Deep Medicine shows us how the awesome power of AI can make medicine better, for all the humans involved.”

 


Deep Medicine: How Artificial Intelligence can Make Healthcare Human Again provides a quick overview of AI, in what sectors it is being used, and then proceeds to discuss its application within the different branches of healthcare, including drug discovery, omics, diagnosis from radiography, virtual medicine (telemedicine, chatbots), nutrition, and mental health. Topol brings forth the argument that incorporating AI in executing these mundane and repetitive tasks that generally have solid boundaries of scope, will eventually free up doctors to focus on value-added human-to-human interactions with their patients that machines, no matter how sophisticated, are unlikely capable of duplicating. This book is potentially a great read for not only healthcare workers but also policy makers interested in driving change in the industry. 


Life 3.0: Being Human in the Age of Artificial Intelligence


Author(s):

Max Tegmark

Price Range (USD):

$15.03-15.86 (Hardcover) Buy on Amazon.com

Number of Reviews:

832

Average Review Rating (out of 5)

4.3

Categories

AI

Prerequisites:

N/A

Book Abstract:

How will Artificial Intelligence affect crime, war, justice, jobs, society and our very sense of being human? The rise of AI has the potential to transform our future more than any other technology—and there’s nobody better qualified or situated to explore that future than Max Tegmark, an MIT professor who’s helped mainstream research on how to keep AI beneficial.
 
How can we grow our prosperity through automation without leaving people lacking income or purpose? What career advice should we give today’s kids? How can we make future AI systems more robust, so that they do what we want without crashing, malfunctioning or getting hacked? Should we fear an arms race in lethal autonomous weapons? Will machines eventually outsmart us at all tasks, replacing humans on the job market and perhaps altogether? Will AI help life flourish like never before or give us more power than we can handle?
 
What sort of future do you want? This book empowers you to join what may be the most important conversation of our time. It doesn’t shy away from the full range of viewpoints or from the most controversial issues—from superintelligence to meaning, consciousness and the ultimate physical limits on life in the cosmos.

 


In Life 3.0, Max Tegmark describes the present, future, and distant future possibilities about the impacts of AI accompanied by a discussion on the popular controversies, myths and misconceptions associated with AI, before moving on to a discussion on what we really mean by “intelligence”. 

Overall, Tegmark remains optimistic, stating that technology is responsible for nearly all the improvements in the quality of life since the stone age and it will only continue on that path. At the same time, he convincingly illustrates the necessity for further evaluation of our goals as a society and plans for AI’s integration into our lives—one that can be the most important conversation of our times.

The author breaks down complex concepts with simple comparisons and manages to keep things, and steer clear of exaggerations, with in-depth breakdown of every possible scenario AI might take, both promising as well as grim ones. Regardless of your field of study or profession, the book really makes you ponder what “values” you want machines and AI to have. 

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


Author(s):

Andreas C Muller, Sarah Guido

Price Range (USD):

$49.89 (Paperback) Buy on Amazon.com

Number of Reviews:

157

Average Review Rating (out of 5)

4.2

Categories

Machine Learning; NLP; Python; Artificial Neural Networks (ANN)

Prerequisites:

Python (Basic)

Book Abstract:

“Machine learning has become an integral part of many commercial applications and research projects, but this field is not exclusive to large companies with extensive research teams. If you use Python, even as a beginner, this book will teach you practical ways to build your own machine learning solutions. With all the data available today, machine learning applications are limited only by your imagination.

You’ll learn the steps necessary to create a successful machine-learning application with Python and the scikit-learn library. Authors Andreas Müller and Sarah Guido focus on the practical aspects of using machine learning algorithms, rather than the math behind them. Familiarity with the NumPy and matplotlib libraries will help you get even more from this book.

With this book, you’ll learn:

  • Fundamental concepts and applications of machine learning

  • Advantages and shortcomings of widely used machine learning algorithms

  • How to represent data processed by machine learning, including which data aspects to focus on

  • Advanced methods for model evaluation and parameter tuning

  • The concept of pipelines for chaining models and encapsulating your workflow

  • Methods for working with text data, including text-specific processing techniques

  • Suggestions for improving your machine learning and data science skills”


For individuals familiar with Python who are eager to apply it in genuine Machine Learning applications, this book can definitely lay a solid foundation on the mainstream Machine Learning algorithms. It focusses mostly on Scikit-learn packages with some exposure to numpy, pandas and matplotlib. Additionally, some elements of clustering, feature engineering (PCA), and model performance evaluation metrics are discussed in detail as well, rounding off all the different segments of a typical machine learning pipeline. Deep learning topics such as Artificial Neural Networks (ANN) are covered in brevity, with the focus being in core machine learning.  


Artificial Intelligence in Finance: A Python-Based Guide


Author(s):

Yves Hilpisch

Price Range (USD):

$79.99 (Paperback) Buy on Amazon.com

Number of Reviews:

N/A

Average Review Rating (out of 5)

N/A

Categories

Finance; Python; Machine Learning; Deep Learning; Reinforcement Learning 

Prerequisites:


Book Abstract:

“Many industries have been revolutionized by the widespread adoption of AI and machine learning. The programmatic availability of historical and real-time financial data in combination with techniques from AI and machine learning will also change the financial industry in a fundamental way. This practical book explains how to use AI and machine learning to discover statistical inefficiencies in financial markets and exploit them through algorithmic trading.

Author Yves Hilpisch shows practitioners, students, and academics in both finance and data science how machine and deep learning algorithms can be applied to finance. Thanks to lots of self-contained Python examples, you’ll be able to replicate all results and figures presented in the book.

  • Examine how data is reshaping finance from a theory-driven to a data-driven discipline

  • Understand the major possibilities, consequences, and resulting requirements of AI-first finance

  • Get up to speed on the tools, skills, and major use cases to apply AI in finance yourself

  • Apply neural networks and reinforcement learning to discover statistical inefficiencies in financial markets

  • Delve into the concepts of the technological singularity and the financial singularity”


Finance is a huge application area for Machine Learning. For example, one of the more traditional applications lies stock price prediction with time series and transactional data. Artificial Intelligence in Finance is an unreleased book as of yet, with pre-orders being accepted leading upto its expected release in Q4 2020. There isn’t much published information or reviews on the book, but it definitely addresses a market gap for a good reference book for AI applications in Finance. 

Grokking Algorithms: An illustrated guide for programmers and other curious people


Author(s):

Aditya Bhargava

Price Range (USD):

$38.74-39.38 (Paperback) Buy on Amazon.com

Number of Reviews:

246

Average Review Rating (out of 5)

4.6

Categories

Data Structures; Algorithms; Machine Learning

Prerequisites:


Book Abstract:

Summary:

Grokking Algorithms is a fully illustrated, friendly guide that teaches you how to apply common algorithms to the practical problems you face every day as a programmer. You'll start with sorting and searching and, as you build up your skills in thinking algorithmically, you'll tackle more complex concerns such as data compression and artificial intelligence. Each carefully presented example includes helpful diagrams and fully annotated code samples in Python.

Learning about algorithms doesn't have to be boring! Get a sneak peek at the fun, illustrated, and friendly examples you'll find in Grokking Algorithms on Manning Publications' YouTube channel.

Continue your journey into the world of algorithms with Algorithms in Motion, a practical, hands-on video course available exclusively at Manning.com (www.manning.com/livevideo/algorithms-in-motion).

Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications.



About the Technology:

An algorithm is nothing more than a step-by-step procedure for solving a problem. The algorithms you'll use most often as a programmer have already been discovered, tested, and proven. If you want to understand them but refuse to slog through dense multipage proofs, this is the book for you. This fully illustrated and engaging guide makes it easy to learn how to use the most important algorithms effectively in your own programs.

About the Book:

Grokking Algorithms is a friendly take on this core computer science topic. In it, you'll learn how to apply common algorithms to the practical programming problems you face every day. You'll start with tasks like sorting and searching. As you build up your skills, you'll tackle more complex problems like data compression and artificial intelligence. Each carefully presented example includes helpful diagrams and fully annotated code samples in Python. By the end of this book, you will have mastered widely applicable algorithms as well as how and when to use them.

What's Inside:

  • Covers search, sort, and graph algorithms

  • Over 400 pictures with detailed walkthroughs

  • Performance trade-offs between algorithms

  • Python-based code samples


About the Reader:

This easy-to-read, picture-heavy introduction is suitable for self-taught programmers, engineers, or anyone who wants to brush up on algorithms.

About the Author:

Aditya Bhargava is a Software Engineer with a dual background in Computer Science and Fine Arts. He blogs on programming at adit.io


What makes this book stand out is its visual approach to teaching algorithms. With the plethora of illustrations in the book, this book is a must-read for ones who have a visual learning method and want to learn coding. This book is geared towards beginner-level programmers and individuals without a background in Computer Science and advanced Software Engineers or individuals with a degree in Computer Science and well versed in algorithms probably will likely find this book too high level. 

The focus of the book is definitely data structures and algorithms, more so than machine learning specifically. Although it doesn’t cover all the data structure and algorithms you generally see within Computer Science, but the ones it does cover, it does so very well!

Data structure and algorithms is definitely a very interesting knowledge area, especially at the higher level topics, but can quickly grow to be complex and hard to visualize, especially if not explained very well - and that is where this book stands out.

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


Author(s):

Aurélien Géron

Price Range (USD):

$25.27-36.10 (Paperback) Buy on Amazon.com

Number of Reviews:

786

Average Review Rating (out of 5)

4.5

Categories

Machine Learning; Deep Learning; Deep Neural Networks (DNN); Convolutional Neural Networks (CNN); Recurrent Neural Networks (RNN)

Prerequisites:

Python (Basic – Intermediate); Basic Statistics

Book Abstract:

“Through a series of recent breakthroughs, deep learning has boosted the entire field of machine learning. Now, even programmers who know close to nothing about this technology can use simple, efficient tools to implement programs capable of learning from data. This practical book shows you how.

By using concrete examples, minimal theory, and two production-ready Python frameworks— Scikit-learn and TensorFlow—author Aurélien Géron helps you gain an intuitive understanding of the concepts and tools for building intelligent systems. You’ll learn a range of techniques, starting with simple linear regression and progressing to deep neural networks. With exercises in each chapter to help you apply what you’ve learned, all you need is programming experience to get started.

  • 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

  • Apply practical code examples without acquiring excessive machine learning theory or algorithm details


In the first few pages of the book, Géron provides an overview of Machine Learning (ML) systems, the main challenges around ML models, and perspectives on testing and validation of models.

This is followed by a few hands-on, end-to-end ML projects implemented with Scikit-learn libraries. Through the chapters, the author provides guidance on how you should approach framing a given problem, selecting the performance evaluation measure(s), extracting the data, performing exploratory data analysis and visualization, preparing and cleaning the data, selecting and training the model, tuning the model hyperparameters, and concluding with topics on launching, monitoring, and maintaining the system.

Multiple ML models are discussed, including, but not limited to, linear and logistic regressions, Support Vector Machines (SVMs), decision trees, ensemble methods (GBM, random forest), as well as feature engineering topics such as dimensionality reduction using Principal Component Analysis (PCA).

Conceptual introduction to neural nets is also included in the book in the form of Multi-Level Perceptron (MLP), Deep Neural Network (DNN), Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNN’s) with Tensorflow.

Hands-On Machine Learning with Scikit-Learn and TensorFlow is better suited for the advanced novices. The book provides good practical tips and hints to help you to build good instincts as a data scientist. It is best if you are already comfortable with coding in Python; and without a background in statistics, you could be prone to making poor choices once you start modelling the problems. 

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