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| <title classes #id> | <title classes #id> | ||
| - | Contents | + | Contents (hardcover) |
| </title> | </title> | ||
| - | * Foreword | + | * [[http://bit.ly/theMLbook-Preface|Preface]] |
| - | - Introduction | + | - [[http://bit.ly/theMLbook-Chapter-1|Introduction]] |
| * What is Machine Learning | * What is Machine Learning | ||
| * Types of Learning | * Types of Learning | ||
| Line 15: | Line 15: | ||
| * How Supervised Learning Works | * How Supervised Learning Works | ||
| * Why the Model Works on New Data | * Why the Model Works on New Data | ||
| - | - Notation and Definitions | + | - [[http://bit.ly/theMLbook-Chapter-2|Notation and Definitions]] |
| * Notation | * Notation | ||
| * Scalars, Vectors, and Sets | * Scalars, Vectors, and Sets | ||
| Line 30: | Line 30: | ||
| * Instance-Based vs Model-Based Learning | * Instance-Based vs Model-Based Learning | ||
| * Shallow vs Deep Learning | * Shallow vs Deep Learning | ||
| - | - Fundamental Algorithms | + | - [[http://bit.ly/theMLbook-Chapter-3|Fundamental Algorithms]] |
| * Linear Regression | * Linear Regression | ||
| * Logistic Regression | * Logistic Regression | ||
| Line 36: | Line 36: | ||
| * Support Vector Machine | * Support Vector Machine | ||
| * k-Nearest Neighbors | * k-Nearest Neighbors | ||
| - | - Anatomy of a Learning Algorithm | + | - [[http://bit.ly/theMLbook-Chapter-4|Anatomy of a Learning Algorithm]] |
| * Building Blocks of a Learning Algorithm | * Building Blocks of a Learning Algorithm | ||
| * Gradient Descent | * Gradient Descent | ||
| * How Machine Learning Engineers Work | * How Machine Learning Engineers Work | ||
| * Learning Algorithms' Particularities | * Learning Algorithms' Particularities | ||
| - | - Basic Practice | + | - [[http://bit.ly/theMLbook-Chapter-5|Basic Practice]] |
| * Feature Engineering | * Feature Engineering | ||
| * One-Hot Encoding | * One-Hot Encoding | ||
| Line 60: | Line 60: | ||
| * Area under the ROC Curve (AUC) | * Area under the ROC Curve (AUC) | ||
| * Hyperparameter Tuning | * Hyperparameter Tuning | ||
| - | - Neural Networks and Deep Learning | + | * Cross-Validation |
| + | - [[http://bit.ly/theMLbook-Chapter-6|Neural Networks and Deep Learning]] | ||
| * Neural Networks | * Neural Networks | ||
| * Multilayer Perceptron Example | * Multilayer Perceptron Example | ||
| Line 67: | Line 68: | ||
| * Convolutional Neural Network | * Convolutional Neural Network | ||
| * Recurrent Neural Network | * Recurrent Neural Network | ||
| - | - Problems and Solutions | + | - [[http://bit.ly/theMLbook-Chapter-7|Problems and Solutions]] |
| * Kernel Regression | * Kernel Regression | ||
| * Multiclass Classification | * Multiclass Classification | ||
| Line 81: | Line 82: | ||
| * One-Shot Learning | * One-Shot Learning | ||
| * Zero-Shot Learning | * Zero-Shot Learning | ||
| - | - Advanced Practice | + | - [[http://bit.ly/theMLbook-Chapter-8|Advanced Practice]] |
| * Handling Imbalanced Datasets | * Handling Imbalanced Datasets | ||
| * Combining Models | * Combining Models | ||
| Line 89: | Line 90: | ||
| * Handling Multiple Outputs | * Handling Multiple Outputs | ||
| * Transfer Learning | * Transfer Learning | ||
| - | - Unsupervised Learning | + | * Algorithmic Efficiency |
| + | - [[http://bit.ly/theMLbook-Chapter09|Unsupervised Learning]] | ||
| * Density Estimation | * Density Estimation | ||
| * Clustering | * Clustering | ||
| Line 113: | Line 115: | ||
| * Generalized Linear Models | * Generalized Linear Models | ||
| * Probabilistic Graphical Models | * Probabilistic Graphical Models | ||
| - | * Markov chain Monte Carlo | + | * Markov Chain Monte Carlo |
| * Genetic Algorithms | * Genetic Algorithms | ||
| * Reinforcement Learning | * Reinforcement Learning | ||
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