The Hundred-Page Machine Learning Book
The Hundred-Page Machine Learning Book (PDF + EPUB + extra PDF formats)
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The Hundred-Page Machine Learning Book

This book is 100% complete

Completed on 2019-02-22

About the Book

Peter Norvig, Research Director at Google, co-author of AIMA, the most popular AI textbook in the world: "Burkov has undertaken a very useful but impossibly hard task in reducing all of machine learning to 100 pages. He succeeds well in choosing the topics — both theory and practice — that will be useful to practitioners, and for the reader who understands that this is the first 100 (or actually 150) pages you will read, not the last, provides a solid introduction to the field."

Aurélien Géron, Senior AI Engineer, author of the bestseller Hands-On Machine Learning with Scikit-Learn and TensorFlow"The breadth of topics the book covers is amazing for just 100 pages (plus few bonus pages!). Burkov doesn't hesitate to go into the math equations: that's one thing that short books usually drop. I really liked how the author explains the core concepts in just a few words. The book can be very useful for newcomers in the field, as well as for old-timers who can gain from such a broad view of the field."

Gareth James, Professor of Data Sciences and Operations, co-author of the bestseller An Introduction to Statistical Learning, with Applications in R"This is a compact “how to do data science” manual and I predict it will become a go-to resource for academics and practitioners alike. At 100 pages (or a little more), the book is short enough to read in a single sitting. Yet, despite its length, it covers all the major machine learning approaches, ranging from classical linear and logistic regression, through to modern support vector machines, deep learning, boosting, and random forests. There is also no shortage of details on the various approaches and the interested reader can gain further information on any particular method via the innovative companion book wiki. The book does not assume any high level mathematical or statistical training or even programming experience, so should be accessible to almost anyone willing to invest the time to learn about these methods. It should certainly be required reading for anyone starting a PhD program in this area and will serve as a useful reference as they progress further. Finally, the book illustrates some of the algorithms using Python code, one of the most popular coding languages for machine learning. I would highly recommend “The Hundred-Page Machine Learning Book” for both the beginner looking to learn more about machine learning and the experienced practitioner seeking to extend their knowledge base."

***

As its title says, it's the hundred-page machine learning book. It was written by an expert in machine learning holding a Ph.D. in Artificial Intelligence with almost two decades of industry experience in computer science and hands-on machine learning.

This is a unique book in many aspects. It is the first successful attempt to write an easy to read book on machine learning that isn't afraid of using math. It's also the first attempt to squeeze a wide range of machine learning topics in a systematic way and without loss in quality.

The book contains only those parts of the huge body of material on machine learning developed since the 1960s that have proven to have a significant practical value. A beginner in machine learning will find in this book just enough details to get a comfortable level of understanding of the field and start asking the right questions. Practitioners with experience will use this book as a collection of pointers to the directions of further self-improvement.

The book also comes in handy when brainstorming at the beginning of a project, when you try to answer the question whether a given technical or business problem is "machine-learnable" and, if yes, which techniques you should try to solve it.

The book comes with a wiki which contains pages that extend some book chapters with additional information: Q&A, code snippets, further reading, tools, and other relevant resources. Thanks to the continuously updated wiki this book like a good wine keeps getting better after you buy it.

About the Author

Andriy Burkov
Andriy Burkov

Andriy Burkov holds a PhD in Artificial Intelligence, he works as a senior data scientist and machine learning team leader at Gartner.

Reader Testimonials

Karolis Urbonas
Karolis Urbonas

Head of Data Science at Amazon

This book is a great introduction to machine learning from a world-class practitioner and LinkedIn superstar Andriy Burkov. He managed to find a good balance between the math of the algorithms, intuitive visualizations, and easy-to-read explanations. This book will benefit the newcomers to the field as a thorough introduction to the fundamentals of machine learning, while the experienced professionals will definitely enjoy the practical recommendations from Andriy's rich experience in the field.

Chao Han
Chao Han

VP, Head of R&D at Lucidworks

I wish such a book existed when I was a statistics graduate student trying to learn about machine learning. There is the right amount of math which demystify the centerpiece of an algorithm with succinct but very clear descriptions. I'm also impressed by the widespread coverage and good choices of important methods as an introductory book (not all machine learning books mention things like learning to rank or metric learning). Highly recommended to STEM major students.

Sujeet Varakhedi
Sujeet Varakhedi

Head of Engineering at eBay

Whether you want to become a machine learning practitioner or looking for an everyday resource, Andriy's book does a fantastic job of cutting the noise and hitting the tracks and full speed from the first page. It manages to structure all the important concepts from foundations to applications into a relatively quick read and leave the reader engaged at all times.

Deepak Agarwal
Deepak Agarwal

VP of AI at LinkedIn

This book provides a great practical guide to get started and execute on ML within a few days without necessarily knowing much about ML apriori. The first five chapters are enough to get you started and the next few chapters provide you a good feel of more advanced topics to pursue. A wonderful book for engineers who want to incorporate ML in their day-to-day work without necessarily spending an enormous amount of time going through a formal degree program.

Vincent Pollet
Vincent Pollet

Head of Research at Nuance

The Hundred-Page Machine Learning Book is an excellent read to get started with Machine Learning. In his book, Andriy Burkov distills the ubiquitous material on Machine Learning into concise and well-balanced intuitive, theoretical and practical elements that bring beginners, managers, and practitioners many life hacks.

Table of Contents

Preface


1 Introduction

1.1 What is Machine Learning

1.2 Types of Learning

1.2.1 Supervised Learning

1.2.2 Unsupervised Learning

1.2.3 Semi-Supervised Learning

1.2.4 Reinforcement Learning

1.3 How Supervised Learning Works

1.4 Why the Model Works on New Data


2 Notation and Definitions

2.1 Notation

2.1.1 Data Structures

2.1.2 Capital Sigma Notation

2.1.3 Capital Pi Notation

2.1.4 Operations on Sets

2.1.5 Operations on Vectors

2.1.6 Functions

2.1.7 Max and Arg Max

2.1.8 Assignment Operator

2.1.9 Derivative and Gradient

2.2 Random Variable

2.3 Unbiased Estimators

2.4 Bayes’ Rule

2.5 Parameter Estimation

2.6 Parameters vs. Hyperparameters

2.7 Classification vs. Regression

2.8 Model-Based vs. Instance-Based Learning

2.9 Shallow vs. Deep Learning


3 Fundamental Algorithms

3.1 Linear Regression

3.1.1 Problem Statement

3.1.2 Solution

3.2 Logistic Regression

3.2.1 Problem Statement

3.2.2 Solution

3.3 Decision Tree Learning

3.3.1 Problem Statement

3.3.2 Solution

3.4 Support Vector Machine

3.4.1 Dealing with Noise

3.4.2 Dealing with Inherent Non-Linearity

3.5 k-Nearest Neighbors


4 Anatomy of a Learning Algorithm

4.1 Building Blocks of a Learning Algorithm

4.2 Gradient Descent

4.3 How Machine Learning Engineers Work

4.4 Learning Algorithms’ Particularities


5 Basic Practice

5.1 Feature Engineering

5.1.1 One-Hot Encoding

5.1.2 Binning

5.1.3 Normalization

5.1.4 Standardization

5.1.5 Dealing with Missing Features

5.1.6 Data Imputation Techniques

5.2 Learning Algorithm Selection

5.3 Three Sets

5.4 Underfitting and Overfitting

5.5 Regularization

5.6 Model Performance Assessment

5.6.1 Confusion Matrix

5.6.2 Precision/Recall

5.6.3 Accuracy

5.6.4 Cost-Sensitive Accuracy

5.6.5 Area under the ROC Curve (AUC)

5.7 Hyperparameter Tuning

5.7.1 Cross-Validation


6 Neural Networks and Deep Learning

6.1 Neural Networks

6.1.1 Multilayer Perceptron Example

6.1.2 Feed-Forward Neural Network Architecture

6.2 Deep Learning

6.2.1 Convolutional Neural Network

6.2.2 Recurrent Neural Network


7 Problems and Solutions

7.1 Kernel Regression

7.2 Multiclass Classification

7.3 One-Class Classification

7.4 Multi-Label Classification

7.5 Ensemble Learning

7.5.1 Boosting and Bagging

7.5.2 Random Forest

7.5.3 Gradient Boosting

7.6 Learning to Label Sequences

7.7 Sequence-to-Sequence Learning

7.8 Active Learning

7.9 Semi-Supervised Learning

7.10 One-Shot Learning

7.11 Zero-Shot Learning


8 Advanced Practice

8.1 Handling Imbalanced Datasets

8.2 Combining Models

8.3 Training Neural Networks

8.4 Advanced Regularization

8.5 Handling Multiple Inputs

8.6 Handling Multiple Outputs

8.7 Transfer Learning

8.8 Algorithmic Efficiency


9 Unsupervised Learning

9.1 Density Estimation

9.2 Clustering

9.2.1 K-Means

9.2.2 DBSCAN and HDBSCAN

9.2.3 Determining the Number of Clusters

9.2.4 Other Clustering Algorithms

9.3 Dimensionality Reduction

9.3.1 Principal Component Analysis

9.3.2 UMAP

9.4 Outlier Detection


10 Other Forms of Learning

10.1 Metric Learning

10.2 Learning to Rank

10.3 Learning to Recommend

10.3.1 Factorization Machines

10.3.2 Denoising Autoencoders

10.4 Self-Supervised Learning: Word Embeddings


11 Conclusion

11.1 Topic Modeling

11.2 Gaussian Processes

11.3 Generalized Linear Models

11.4 Probabilistic Graphical Models

11.5 Markov Chain Monte Carlo

11.6 Genetic Algorithms

11.7 Reinforcement Learning


Index

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