The Hundred-Page Machine Learning Book by Andriy Burkov, ISBN-13: 978-1999579500

The Hundred-Page Machine Learning Book by Andriy Burkov, ISBN-13: 978-1999579500

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• Publisher: ‎ Andriy Burkov (January 13, 2019)
• Language: ‎ English
• ‎160 pages
• ISBN-10: ‎ 199957950X
• ISBN-13: ‎ 978-1999579500

Become a machine learning expert. Step up your career.

Today’s top companies undergo the most significant transformation since industrialization. Artificial Intelligence disrupts industries, the way we work, think, interact. Gartner predicts that AI will fully automate 69% of routine work currently done by managers by 2024. PricewaterhouseCoopers predicts that AI will add $16trn to the global economy by 2030. Machine Learning is what drives AI. Experts in this domain are rare, employers fight for the ML-skilled talent. With this book, you will learn how Machine Learning works. A hundred pages from now, you will be ready to build complex AI systems, pass an interview or start your own business.

All you need to know about Machine Learning in a hundred pages

Supervised and unsupervised learning, support vector machines, neural networks, ensemble methods, gradient descent, cluster analysis and dimensionality reduction, autoencoders and transfer learning, feature engineering and hyperparameter tuning! Math, intuition, illustrations, all in just a hundred pages!

You will enjoy the book if you are:

• a software engineer or a scientist who wants to become a machine learning engineer or a data scientist
• a data scientist trying to stay on the edge of the state-of-the-art and deepen their ML expertise
• a manager who wants to feel confident while talking about AI with engineers and product people
• a curious person looking to find out how machine learning works and maybe build something new

Concise and to the point — the book can be read during a week. During that week, you will learn almost everything the modern machine learning has to offer. The author and other practitioners have spent years learning these concepts.

“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 as the first 100 (or actually 150) pages you will read, not the last, provides a solid introduction to the field.” — Peter Norvig, Research Director at Google, author of the best-selling textbook Artificial Intelligence: A Modern Approach

“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.” — Aurélien Géron, Senior AI Engineer, author of the bestseller Hands-On Machine Learning with Scikit-Learn and TensorFlow

Table of Contents:

Foreword xv
Preface xvii
Who This Book is For . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xvii
How to Use This Book . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xviii
Should You Buy This Book? . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xviii
1 Introduction 1
1.1 What is Machine Learning . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
1.2 Types of Learning . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
1.2.1 Supervised Learning . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
1.2.2 Unsupervised Learning . . . . . . . . . . . . . . . . . . . . . . . . . . . 2
1.2.3 Semi-Supervised Learning . . . . . . . . . . . . . . . . . . . . . . . . . 2
1.2.4 Reinforcement Learning . . . . . . . . . . . . . . . . . . . . . . . . . . 3
1.3 How Supervised Learning Works . . . . . . . . . . . . . . . . . . . . . . . . . 3
1.4 Why the Model Works on New Data . . . . . . . . . . . . . . . . . . . . . . . . 7
2 Notation and Definitions 9
2.1 Notation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9
2.1.1 Data Structures . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9
2.1.2 Capital Sigma Notation . . . . . . . . . . . . . . . . . . . . . . . . . . 10
2.1.3 Capital Pi Notation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11
2.1.4 Operations on Sets . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11
2.1.5 Operations on Vectors . . . . . . . . . . . . . . . . . . . . . . . . . . . 11
2.1.6 Functions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12
2.1.7 Max and Arg Max . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13
2.1.8 Assignment Operator . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14
2.1.9 Derivative and Gradient . . . . . . . . . . . . . . . . . . . . . . . . . . 14
2.2 Random Variable . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15
2.3 Unbiased Estimators . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17
2.4 Bayes’ Rule . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17
2.5 Parameter Estimation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17
2.6 Parameters vs. Hyperparameters . . . . . . . . . . . . . . . . . . . . . . . . . 18
2.7 Classification vs. Regression . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19
2.8 Model-Based vs. Instance-Based Learning . . . . . . . . . . . . . . . . . . . . 19
2.9 Shallow vs. Deep Learning . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20
3 Fundamental Algorithms 21
3.1 Linear Regression . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21
3.1.1 Problem Statement . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21
3.1.2 Solution . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23
3.2 Logistic Regression . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25
3.2.1 Problem Statement . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25
3.2.2 Solution . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26
3.3 Decision Tree Learning . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27
3.3.1 Problem Statement . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27
3.3.2 Solution . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 28
3.4 Support Vector Machine . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 30
3.4.1 Dealing with Noise . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 31
3.4.2 Dealing with Inherent Non-Linearity . . . . . . . . . . . . . . . . . . . 32
3.5 k-Nearest Neighbors . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 34
4 Anatomy of a Learning Algorithm 35
4.1 Building Blocks of a Learning Algorithm . . . . . . . . . . . . . . . . . . . . . 35
4.2 Gradient Descent . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 36
4.3 How Machine Learning Engineers Work . . . . . . . . . . . . . . . . . . . . . 41
4.4 Learning Algorithms’ Particularities . . . . . . . . . . . . . . . . . . . . . . . . 41
5 Basic Practice 43
5.1 Feature Engineering . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 43
5.1.1 One-Hot Encoding . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 44
5.1.2 Binning . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 44
5.1.3 Normalization . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 45
5.1.4 Standardization . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 45
5.1.5 Dealing with Missing Features . . . . . . . . . . . . . . . . . . . . . . . 46
5.1.6 Data Imputation Techniques . . . . . . . . . . . . . . . . . . . . . . . . 47
5.2 Learning Algorithm Selection . . . . . . . . . . . . . . . . . . . . . . . . . . . 47
5.3 Three Sets . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 49
5.4 Underfitting and Overfitting . . . . . . . . . . . . . . . . . . . . . . . . . . . . 51
5.5 Regularization . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 52
5.6 Model Performance Assessment . . . . . . . . . . . . . . . . . . . . . . . . . . 54
5.6.1 Confusion Matrix . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 55
5.6.2 Precision/Recall . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 55
5.6.3 Accuracy . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 56
5.6.4 Cost-Sensitive Accuracy . . . . . . . . . . . . . . . . . . . . . . . . . . 57
5.6.5 Area under the ROC Curve (AUC) . . . . . . . . . . . . . . . . . . . . . 58
5.7 Hyperparameter Tuning . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 58
5.7.1 Cross-Validation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 60
6 Neural Networks and Deep Learning 61
6.1 Neural Networks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 61
6.1.1 Multilayer Perceptron Example . . . . . . . . . . . . . . . . . . . . . . 62
6.1.2 Feed-Forward Neural Network Architecture . . . . . . . . . . . . . . . 64
6.2 Deep Learning . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 65
6.2.1 Convolutional Neural Network . . . . . . . . . . . . . . . . . . . . . . 65
6.2.2 Recurrent Neural Network . . . . . . . . . . . . . . . . . . . . . . . . . 72
7 Problems and Solutions 77
7.1 Kernel Regression . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 77
7.2 Multiclass Classification . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 78
7.3 One-Class Classification . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 79
7.4 Multi-Label Classification . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 81
7.5 Ensemble Learning . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 83
7.5.1 Boosting and Bagging . . . . . . . . . . . . . . . . . . . . . . . . . . . 83
7.5.2 Random Forest . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 84
7.5.3 Gradient Boosting . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 85
7.6 Learning to Label Sequences . . . . . . . . . . . . . . . . . . . . . . . . . . . . 87
7.7 Sequence-to-Sequence Learning . . . . . . . . . . . . . . . . . . . . . . . . . . 88
7.8 Active Learning . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 89
7.9 Semi-Supervised Learning . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 91
7.10 One-Shot Learning . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 94
7.11 Zero-Shot Learning . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 95
8 Advanced Practice 97
8.1 Handling Imbalanced Datasets . . . . . . . . . . . . . . . . . . . . . . . . . . 97
8.2 Combining Models . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 99
8.3 Training Neural Networks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 99
8.4 Advanced Regularization . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 100
8.5 Handling Multiple Inputs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 101
8.6 Handling Multiple Outputs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 102
8.7 Transfer Learning . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 102
8.8 Algorithmic Efficiency . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 104
9 Unsupervised Learning 107
9.1 Density Estimation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 107
9.2 Clustering . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 109
9.2.1 K-Means . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 110
9.2.2 DBSCAN and HDBSCAN . . . . . . . . . . . . . . . . . . . . . . . . . . 111
9.2.3 Determining the Number of Clusters . . . . . . . . . . . . . . . . . . . 112
9.2.4 Other Clustering Algorithms . . . . . . . . . . . . . . . . . . . . . . . . 115
9.3 Dimensionality Reduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 118
9.3.1 Principal Component Analysis . . . . . . . . . . . . . . . . . . . . . . . 119
9.3.2 UMAP . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 119
9.4 Outlier Detection . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 121
10 Other Forms of Learning 123
10.1 Metric Learning . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 123
10.2 Learning to Rank . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 125
10.3 Learning to Recommend . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 127
10.3.1 Factorization Machines . . . . . . . . . . . . . . . . . . . . . . . . . . . 128
10.3.2 Denoising Autoencoders . . . . . . . . . . . . . . . . . . . . . . . . . . 130
10.4 Self-Supervised Learning: Word Embeddings . . . . . . . . . . . . . . . . . . . 131
11 Conclusion 133
11.1 What Wasn’t Covered . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 133
11.1.1 Topic Modeling . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 133
11.1.2 Gaussian Processes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 134
11.1.3 Generalized Linear Models . . . . . . . . . . . . . . . . . . . . . . . . . 134
11.1.4 Probabilistic Graphical Models . . . . . . . . . . . . . . . . . . . . . . 134
11.1.5 Markov Chain Monte Carlo . . . . . . . . . . . . . . . . . . . . . . . . 134
11.1.6 Generative Adversarial Networks . . . . . . . . . . . . . . . . . . . . . 135
11.1.7 Genetic Algorithms . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 135
11.1.8 Reinforcement Learning . . . . . . . . . . . . . . . . . . . . . . . . . . 136
11.2 Acknowledgements . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 136
Index 137

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