The Hundred-Page Machine Learning Book

The Hundred-Page Machine Learning Book

by Andriy Burkov
The Hundred-Page Machine Learning Book

The Hundred-Page Machine Learning Book

by Andriy Burkov

Hardcover(Hard Cover ed.)

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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.

Product Details

ISBN-13: 9781999579517
Publisher: Andriy Burkov
Publication date: 01/11/2019
Edition description: Hard Cover ed.
Pages: 160
Sales rank: 159,252
Product dimensions: 7.50(w) x 9.25(h) x 0.56(d)

Table of Contents


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


9.2.3 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


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