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- | {{keywords>Association Rule Learning}} | + | {{keywords>contents}} |
<title classes #id> | <title classes #id> | ||
- | Association Rule Learning | + | 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 | ||
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* 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 | ||
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* 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 | ||
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* 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 | ||
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* 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 | ||
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* 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 | ||
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* 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 | ||
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* 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 | ||
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* 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 |