12 Best Books For Machine Learning (2022 Updated)

Discover valuable machine learning techniques you can understand and apply using just high-school math. In Grokking Machine Learning you will learn: Supervised algorithms for classifying and splitting data Methods for cleaning and simplifying data Machine learning packages and tools Neural networks and ensemble methods for complex datasets Grokking Machine Learning teaches you how to apply ML to your projects using only standard Python code and high school-level math.

No specialist knowledge is required to tackle the hands-on exercises using Python and readily available machine learning tools. Packed with easy-to-follow Python-based exercises and mini-projects, this book sets you on the path to becoming a machine learning expert.

Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications. About the technology Discover powerful machine learning techniques you can understand and apply using only high school math! Put simply, machine learning is a set of techniques for data analysis based on algorithms that deliver better results as you give them more data.

ML powers many cutting-edge technologies, such as recommendation systems, facial recognition software, smart speakers, and even self-driving cars. This unique book introduces the core concepts of machine learning, using relatable examples, engaging exercises, and crisp illustrations.

About the book Grokking Machine Learning presents machine learning algorithms and techniques in a way that anyone can understand. This book skips the confused academic jargon and offers clear explanations that require only basic algebra.

As you go, you’ll build interesting projects with Python, including models for spam detection and image recognition. You’ll also pick up practical skills for cleaning and preparing data. What’s inside Supervised algorithms for classifying and splitting data Methods for cleaning and simplifying data Machine learning packages and tools Neural networks and ensemble methods for complex datasets About the reader For readers who know basic Python.

No machine learning knowledge necessary. About the author Luis G. Serrano is a research scientist in quantum artificial intelligence. Previously, he was a Machine Learning Engineer at Google and Lead Artificial Intelligence Educator at Apple.

Table of Contents 1 What is machine learning? It is common sense, except done by a computer 2 Types of machine learning 3 Drawing a line close to our points: Linear regression 4 Optimizing the training process: Underfitting, overfitting, testing, and regularization 5 Using lines to split our points: The perceptron algorithm 6 A continuous approach to splitting points: Logistic classifiers 7 How do you measure classification models? Accuracy and its friends 8 Using probability to its maximum: The naive Bayes model 9 Splitting data by asking questions: Decision trees 10 Combining building blocks to gain more power: Neural networks 11 Finding boundaries with style: Support vector machines and the kernel method 12 Combining models to maximize results: Ensemble learning 13 Putting it all in practice: A real-life example of data engineering and machine learning.

Printed in full color! Unlock the groundbreaking advances of deep learning with this extensively revised new edition of the bestselling original. Learn directly from the creator of Keras and master practical Python deep learning techniques that are easy to apply in the real world.

In Deep Learning with Python, Second Edition you will learn: Deep learning from first principles Image classification and image segmentation Timeseries forecasting Text classification and machine translation Text generation, neural style transfer, and image generation Full color printing throughout Deep Learning with Python has taught thousands of readers how to put the full capabilities of deep learning into action.

This extensively revised full color second edition introduces deep learning using Python and Keras, and is loaded with insights for both novice and experienced ML practitioners. You’ll learn practical techniques that are easy to apply in the real world, and important theory for perfecting neural networks.

Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications. About the technology Recent innovations in deep learning unlock exciting new software capabilities like automated language translation, image recognition, and more.

Deep learning is quickly becoming essential knowledge for every software developer, and modern tools like Keras and TensorFlow put it within your reach—even if you have no background in mathematics or data science.

This book shows you how to get started. About the book Deep Learning with Python, Second Edition introduces the field of deep learning using Python and the powerful Keras library. In this revised and expanded new edition, Keras creator François Chollet offers insights for both novice and experienced machine learning practitioners.

You’ll quickly pick up the skills you need to start developing deep-learning applications. What’s inside Deep learning from first principles Image classification and image segmentation Time series forecasting Text classification and machine translation Text generation, neural style transfer, and image generation Full color printing throughout About the reader For readers with intermediate Python skills.

No previous experience with Keras, TensorFlow, or machine learning is required. About the author François Chollet is a software engineer at Google and creator of the Keras deep-learning library. Table of Contents 1 What is deep learning? 2 The mathematical building blocks of neural networks 3 Introduction to Keras and TensorFlow 4 Getting started with neural networks: Classification and regression 5 Fundamentals of machine learning 6 The universal workflow of machine learning 7 Working with Keras: A deep dive 8 Introduction to deep learning for computer vision 9 Advanced deep learning for computer vision 10 Deep learning for timeseries 11 Deep learning for text 12 Generative deep learning 13 Best practices for the real world 14 Conclusions.

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.

It covers the basics and abstractions in great detail. I hope this book becomes your extended reference document. ” —Soumith Chintala, co-creator of PyTorch Key Features Written by Py – Torch’s creator and key contributors Develop deep learning models in a familiar Pythonic way Use PyTorch to build an image classifier for cancer detection Diagnose problems with your neural network and improve training with data augmentation Purchase of the print book includes a free eBook in PDF, Kindle, and e – Pub formats from Manning Publications.

PyTorch puts these superpowers in your hands. Instantly familiar to anyone who knows Python data tools like Num – Py and Scikit-learn, PyTorch simplifies deep learning without sacrificing advanced features.

It’s great for building quick models, and it scales smoothly from laptop to enterprise. Deep Learning with PyTorch teaches you to create deep learning and neural network systems with PyTorch. This practical book gets you to work right away building a tumor image classifier from scratch.

After covering the basics, you’ll learn best practices for the entire deep learning pipeline, tackling advanced projects as your PyTorch skills become more sophisticated. All code samples are easy to explore in downloadable Jupyter notebooks.

What You Will Learn Understanding deep learning data structures such as tensors and neural networks Best practices for the PyTorch Tensor API, loading data in Python, and visualizing results Implementing modules and loss functions Utilizing pretrained models from PyTorch Hub Methods for training networks with limited inputs Sifting through unreliable results to diagnose and fix problems in your neural network Improve your results with augmented data, better model architecture, and fine tuning This Book Is Written For For Python programmers with an interest in machine learning.

No experience with PyTorch or other deep learning frameworks is required. About The Authors Eli Stevens has worked in Silicon Valley for the past 15 years as a software engineer, and the past 7 years as Chief Technical Officer of a startup making medical device software.

Luca Antiga is co-founder and CEO of an AI engineering company located in Bergamo, Italy, and a regular contributor to PyTorch. Thomas Viehmann is a Machine Learning and PyTorch speciality trainer and consultant based in Munich, Germany and a PyTorch core developer.

Table of Contents PART 1 – CORE PYTORCH 1 Introducing deep learning and the PyTorch Library 2 Pretrained networks 3 It starts with a tensor 4 Real-world data representation using tensors 5 The mechanics of learning 6 Using a neural network to fit the data 7 Telling birds from airplanes: Learning from images 8 Using convolutions to generalize PART 2 – LEARNING FROM IMAGES IN THE REAL WORLD: EARLY DETECTION OF LUNG CANCER 9 Using PyTorch to fight cancer 10 Combining data sources into a unified dataset 11 Training a classification model to detect suspected tumors 12 Improving training with metrics and augmentation 13 Using segmentation to find suspected nodules 14 End-to-end nodule analysis, and where to go next PART 3 – DEPLOYMENT 15 Deploying to production.

Printed in full color! Unlock the groundbreaking advances of deep learning with this extensively revised new edition of the bestselling original. Learn directly from the creator of Keras and master practical Python deep learning techniques that are easy to apply in the real world.

In Deep Learning with Python, Second Edition you will learn: Deep learning from first principles Image classification and image segmentation Timeseries forecasting Text classification and machine translation Text generation, neural style transfer, and image generation Full color printing throughout Deep Learning with Python has taught thousands of readers how to put the full capabilities of deep learning into action.

This extensively revised full color second edition introduces deep learning using Python and Keras, and is loaded with insights for both novice and experienced ML practitioners. You’ll learn practical techniques that are easy to apply in the real world, and important theory for perfecting neural networks.

Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications. About the technology Recent innovations in deep learning unlock exciting new software capabilities like automated language translation, image recognition, and more.

Deep learning is quickly becoming essential knowledge for every software developer, and modern tools like Keras and TensorFlow put it within your reach—even if you have no background in mathematics or data science.

This book shows you how to get started. About the book Deep Learning with Python, Second Edition introduces the field of deep learning using Python and the powerful Keras library. In this revised and expanded new edition, Keras creator François Chollet offers insights for both novice and experienced machine learning practitioners.

You’ll quickly pick up the skills you need to start developing deep-learning applications. What’s inside Deep learning from first principles Image classification and image segmentation Time series forecasting Text classification and machine translation Text generation, neural style transfer, and image generation Full color printing throughout About the reader For readers with intermediate Python skills.

No previous experience with Keras, TensorFlow, or machine learning is required. About the author François Chollet is a software engineer at Google and creator of the Keras deep-learning library. Table of Contents 1 What is deep learning? 2 The mathematical building blocks of neural networks 3 Introduction to Keras and TensorFlow 4 Getting started with neural networks: Classification and regression 5 Fundamentals of machine learning 6 The universal workflow of machine learning 7 Working with Keras: A deep dive 8 Introduction to deep learning for computer vision 9 Advanced deep learning for computer vision 10 Deep learning for timeseries 11 Deep learning for text 12 Generative deep learning 13 Best practices for the real world 14 Conclusions.

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.

Summary Deep Learning with Python introduces the field of deep learning using the Python language and the powerful Keras library. Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications.

About the Technology Machine learning has made remarkable progress in recent years. Behind this progress is deep learning—a combination of engineering advances, best practices, and theory that enables a wealth of previously impossible smart applications.

About the Book Deep Learning with Python introduces the field of deep learning using the Python language and the powerful Keras library. You’ll explore challenging concepts and practice with applications in computer vision, natural-language processing, and generative models.

By the time you finish, you’ll have the knowledge and hands-on skills to apply deep learning in your own projects. What’s Inside Deep learning from first principles Setting up your own deep-learning environment Image-classification models Deep learning for text and sequences Neural style transfer, text generation, and image generation About the Reader Readers need intermediate Python skills.

No previous experience with Keras, TensorFlow, or machine learning is required. About the Author François Chollet is one of the most important researchers in modern day deep learning. His groundbreaking work includes the creation of the Keras deep learning library, and major contributions to the TensorFlow framework.

These tools have helped revolutionize and democratize deep learning. François is an AI researcher and Senior Staff Software Engineer at Google. François authored Deep Learning with R alongside J. J. Allaire, and developed the Abstraction and Reasoning Challenge that measures AI skill-acquisition on unknown tasks.

Table of Contents What is deep learning? Our rich and independent history is filled with innovations, including groundbreaking early access programs, DRM-free ebooks, and live learning projects. Manning authors are technology experts, including distinguished academics, industry veterans, and the creators of major tools.

Timeless Manning classics include Francois Chollet’s Deep Learning with Python, Jon Skeet’s C# in Depth, Don Jones’ Learn Windows Powershell in a Month of Lunches, and Chris Richarson’s Microservices Patterns.

Time to flex your machine learning muscles! Take on the carefully designed challenges of the Machine Learning Bookcamp and master essential ML techniques through practical application. – Summary In Machine Learning Bookcamp you will: Collect and clean data for training models Use popular Python tools, including Num – Py, Scikit-Learn, and Tensor – Flow Apply ML to complex datasets with images Deploy ML models to a production-ready environment The only way to learn is to practice! In Machine Learning Bookcamp, you’ll create and deploy Python-based machine learning models for a variety of increasingly challenging projects.

Taking you from the basics of machine learning to complex applications such as image analysis, each new project builds on what you’ve learned in previous chapters. You’ll build a portfolio of business-relevant machine learning projects that hiring managers will be excited to see.

Purchase of the print book includes a free eBook in PDF, Kindle, and e – Pub formats from Manning Publications. About the technology Master key machine learning concepts as you build actual projects! Machine learning is what you need for analyzing customer behavior, predicting price trends, evaluating risk, and much more.

To master ML, you need great examples, clear explanations, and lots of practice. This book delivers all three! About the book Machine Learning Bookcamp presents realistic, practical machine learning scenarios, along with crystal-clear coverage of key concepts.

In it, you’ll complete engaging projects, such as creating a car price predictor using linear regression and deploying a churn prediction service. You’ll go beyond the algorithms and explore important techniques like deploying ML applications on serverless systems and serving models with Kubernetes and Kubeflow.

Dig in, get your hands dirty, and have fun building your ML skills! What’s inside Collect and clean data for training models Use popular Python tools, including Num – Py, Scikit-Learn, and Tensor – Flow Deploy ML models to a production-ready environment About the reader Python programming skills assumed.

No previous machine learning knowledge is required. About the author Alexey Grigorev is a principal data scientist at OLX Group. He runs DataTalks. Club, a community of people who love data. Table of Contents 1 Introduction to machine learning 2 Machine learning for regression 3 Machine learning for classification 4 Evaluation metrics for classification 5 Deploying machine learning models 6 Decision trees and ensemble learning 7 Neural networks and deep learning 8 Serverless deep learning 9 Serving models with Kubernetes and Kubeflow.

Summary – Grokking Deep Learning teaches you to build deep learning neural networks from scratch! In his engaging style, seasoned deep learning expert Andrew Trask shows you the science under the hood, so you grok for yourself every detail of training neural networks.

– Purchase of the print book includes a free eBook in PDF, Kindle, and e – Pub formats from Manning Publications. – About the Technology – Deep learning, a branch of artificial intelligence, teaches computers to learn by using neural networks, technology inspired by the human brain.

Online text translation, self-driving cars, personalized product recommendations, and virtual voice assistants are just a few of the exciting modern advancements possible thanks to deep learning. – About the Book – Grokking Deep Learning teaches you to build deep learning neural networks from scratch! In his engaging style, seasoned deep learning expert Andrew Trask shows you the science under the hood, so you grok for yourself every detail of training neural networks.

Using only Python and its math-supporting library, Num – Py, you’ll train your own neural networks to see and understand images, translate text into different languages, and even write like Shakespeare! When you’re done, you’ll be fully prepared to move on to mastering deep learning frameworks.

– What’s inside – The science behind deep learning – Building and training your own neural networks – Privacy concepts, including federated learning – Tips for continuing your pursuit of deep learning – About the Reader – For readers with high school-level math and intermediate programming skills.

– About the Author – Andrew Trask is a Ph – D student at Oxford University and a research scientist at Deep – Mind. Previously, Andrew was a researcher and analytics product manager at Digital Reasoning, where he trained the world’s largest artificial neural network and helped guide the analytics roadmap for the Synthesys cognitive computing platform.

– Table of Contents – Introducing deep learning: why you should learn it – Fundamental concepts: how do machines learn? Introduction to neural prediction: forward propagation – Introduction to neural learning: gradient descent – Learning multiple weights at a time: generalizing gradient descent – Building your first deep neural network: introduction to backpropagation – How to picture neural networks: in your head and on paper – Learning signal and ignoring noise:introduction to regularization and batching – Modeling probabilities and nonlinearities: activation functions – Neural learning about edges and corners: intro to convolutional neural networks – Neural networks that understand language: king – man + woman == Neural networks that write like Shakespeare: recurrent layers for variable-length data – Introducing automatic optimization: let’s build a deep learning framework – Learning to write like Shakespeare: long short-term memory – Deep learning on unseen data: introducing federated learning – Where to go from here: a brief guide.

Principles and Labs for Deep Learning provides the knowledge and techniques needed to help readers design and develop deep learning models. Deep Learning techniques are introduced through theory, comprehensively illustrated, explained through the TensorFlow source code examples, and analyzed through the visualization of results.

The structured methods and labs provided by Dr. Le enable readers to become proficient in TensorFlow to build deep Convolutional Neural Networks (CNNs) through custom APIs, high-level Keras APIs, Keras Applications, and TensorFlow Hub.

Each chapter has one corresponding Lab with step-by-step instruction to help the reader practice and accomplish a specific learning outcome. Deep Learning has been successfully applied in diverse fields such as computer vision, audio processing, robotics, natural language processing, bioinformatics and chemistry.

Because of the huge scope of knowledge in Deep Learning, a lot of time is required to understand and deploy useful, working applications, hence the importance of this new resource. Both theory lessons and experiments are included in each chapter to introduce the techniques and provide source code examples to practice using them.

All Labs for this book are placed on GitHub to facilitate the download. The book is written based on the assumption that the reader knows basic Python for programming and basic Machine Learning. Introduces readers to the usefulness of neural networks and Deep Learning methods Provides readers with in-depth understanding of the architecture and operation of Deep Convolutional Neural Networks Demonstrates the visualization needed for designing neural networks Provides readers with an in-depth understanding of regression problems, binary classification problems, multi-category classification problems, Variational Auto-Encoder, Generative Adversarial Network, and Object detection.

Summary Deep Learning with Python introduces the field of deep learning using the Python language and the powerful Keras library. Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications.

About the Technology Machine learning has made remarkable progress in recent years. Behind this progress is deep learning—a combination of engineering advances, best practices, and theory that enables a wealth of previously impossible smart applications.

About the Book Deep Learning with Python introduces the field of deep learning using the Python language and the powerful Keras library. You’ll explore challenging concepts and practice with applications in computer vision, natural-language processing, and generative models.

By the time you finish, you’ll have the knowledge and hands-on skills to apply deep learning in your own projects. What’s Inside Deep learning from first principles Setting up your own deep-learning environment Image-classification models Deep learning for text and sequences Neural style transfer, text generation, and image generation About the Reader Readers need intermediate Python skills.

No previous experience with Keras, TensorFlow, or machine learning is required. About the Author François Chollet is one of the most important researchers in modern day deep learning. His groundbreaking work includes the creation of the Keras deep learning library, and major contributions to the TensorFlow framework.

These tools have helped revolutionize and democratize deep learning. François is an AI researcher and Senior Staff Software Engineer at Google. François authored Deep Learning with R alongside J. J. Allaire, and developed the Abstraction and Reasoning Challenge that measures AI skill-acquisition on unknown tasks.

Table of Contents What is deep learning? Our rich and independent history is filled with innovations, including groundbreaking early access programs, DRM-free ebooks, and live learning projects. Manning authors are technology experts, including distinguished academics, industry veterans, and the creators of major tools.

Timeless Manning classics include Francois Chollet’s Deep Learning with Python, Jon Skeet’s C# in Depth, Don Jones’ Learn Windows Powershell in a Month of Lunches, and Chris Richarson’s Microservices Patterns.

Summary – Deep Learning with R introduces the world of deep learning using the powerful Keras library and its R language interface. Purchase of the print book includes a free eBook in PDF, Kindle, and e – Pub formats from Manning Publications.

– About the Technology – Machine learning has made remarkable progress in recent years. Deep-learning systems now enable previously impossible smart applications, revolutionizing image recognition and natural-language processing, and identifying complex patterns in data.

The Keras deep-learning library provides data scientists and developers working in R a state-of-the-art toolset for tackling deep-learning tasks. – About the Book – Deep Learning with R introduces the world of deep learning using the powerful Keras library and its R language interface.

Initially written for Python as Deep Learning with Python by Keras creator and Google AI researcher François Chollet and adapted for R by RStudio founder J. You’ll practice your new skills with R-based applications in computer vision, natural-language processing, and generative models.

– What’s Inside – Deep learning from first principles – Setting up your own deep-learning environment – Image classification and generation – Deep learning for text and sequences – About the Reader – You’ll need intermediate R programming skills.

No previous experience with machine learning or deep learning is assumed. – About the Authors – François Chollet is a deep-learning researcher at Google and the author of the Keras library. Allaire is the founder of RStudio and the author of the R interfaces to Tensor – Flow and Keras.

– Table of Contents – PART 1 – FUNDAMENTALS OF DEEP LEARNINGWhat is deep learning? – Getting started with neural networks – Fundamentals of machine learning – PART 2 – DEEP LEARNING IN PRACTICEDeep learning for computer vision – Deep learning for text and sequences – Advanced deep-learning best practices – Generative deep learning – Conclusions.