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| - | {{keywords>Association Rule Learning}} | + | {{keywords>contents}} |
| <title classes #id> | <title classes #id> | ||
| - | Association Rule Learning | + | Contents (hardcover) |
| </title> | </title> | ||
| + | * [[http://bit.ly/theMLbook-Preface|Preface]] | ||
| + | - [[http://bit.ly/theMLbook-Chapter-1|Introduction]] | ||
| + | * What is Machine Learning | ||
| + | * Types of Learning | ||
| + | * Supervised Learning | ||
| + | * Unsupervised Learning | ||
| + | * Semi-Supervised Learning | ||
| + | * Reinforcement Learning | ||
| + | * How Supervised Learning Works | ||
| + | * Why the Model Works on New Data | ||
| + | - [[http://bit.ly/theMLbook-Chapter-2|Notation and Definitions]] | ||
| + | * Notation | ||
| + | * Scalars, Vectors, and Sets | ||
| + | * Capital Sigma Notation | ||
| + | * Capital Pi Notation | ||
| + | * Operations on Sets | ||
| + | * Max and Arg Max | ||
| + | * Operations on Vectors | ||
| + | * Functions | ||
| + | * Assignment Operator | ||
| + | * Derivative and Gradient | ||
| + | * Random Variable | ||
| + | * Classification vs Regression | ||
| + | * Instance-Based vs Model-Based Learning | ||
| + | * Shallow vs Deep Learning | ||
| + | - [[http://bit.ly/theMLbook-Chapter-3|Fundamental Algorithms]] | ||
| + | * Linear Regression | ||
| + | * Logistic Regression | ||
| + | * Decision Tree Learning | ||
| + | * Support Vector Machine | ||
| + | * k-Nearest Neighbors | ||
| + | - [[http://bit.ly/theMLbook-Chapter-4|Anatomy of a Learning Algorithm]] | ||
| + | * Building Blocks of a Learning Algorithm | ||
| + | * Gradient Descent | ||
| + | * How Machine Learning Engineers Work | ||
| + | * Learning Algorithms' Particularities | ||
| + | - [[http://bit.ly/theMLbook-Chapter-5|Basic Practice]] | ||
| + | * Feature Engineering | ||
| + | * One-Hot Encoding | ||
| + | * Binning | ||
| + | * Normalization | ||
| + | * Standardization | ||
| + | * Dealing With Missing Features | ||
| + | * Data Imputation Techniques | ||
| + | * Learning Algorithm Selection | ||
| + | * Three Sets | ||
| + | * Underfitting and Overfitting | ||
| + | * Regularization | ||
| + | * Model Performance Assessment | ||
| + | * Confusion Matrix | ||
| + | * Accuracy | ||
| + | * Cost-sensitive accuracy | ||
| + | * Precision/Recall | ||
| + | * Area under the ROC Curve (AUC) | ||
| + | * Hyperparameter Tuning | ||
| + | * Cross-Validation | ||
| + | - [[http://bit.ly/theMLbook-Chapter-6|Neural Networks and Deep Learning]] | ||
| + | * Neural Networks | ||
| + | * Multilayer Perceptron Example | ||
| + | * Feed-Forward Neural Network Architecture | ||
| + | * Deep Learning | ||
| + | * Convolutional Neural Network | ||
| + | * Recurrent Neural Network | ||
| + | - [[http://bit.ly/theMLbook-Chapter-7|Problems and Solutions]] | ||
| + | * Kernel Regression | ||
| + | * Multiclass Classification | ||
| + | * One-Class Classification | ||
| + | * Multi-Label Classification | ||
| + | * Ensemble Learning | ||
| + | * Random Forest | ||
| + | * Gradient Boosting | ||
| + | * Learning to Annotate Sequences | ||
| + | * Sequence-to-Sequence Learning | ||
| + | * Active Learning | ||
| + | * Semi-Supervised Learning | ||
| + | * One-Shot Learning | ||
| + | * Zero-Shot Learning | ||
| + | - [[http://bit.ly/theMLbook-Chapter-8|Advanced Practice]] | ||
| + | * Handling Imbalanced Datasets | ||
| + | * Combining Models | ||
| + | * Training Neural Networks | ||
| + | * Advanced Regularization | ||
| + | * Handling Multiple Inputs | ||
| + | * Handling Multiple Outputs | ||
| + | * Transfer Learning | ||
| + | * Algorithmic Efficiency | ||
| + | - [[http://bit.ly/theMLbook-Chapter09|Unsupervised Learning]] | ||
| + | * Density Estimation | ||
| + | * Clustering | ||
| + | * K-means Clustering | ||
| + | * DBSCAN and HDBSCAN | ||
| + | * Determining the Number of Clusters | ||
| + | * Other Clustering Algorithms | ||
| + | * Dimensionality Reduction | ||
| + | * Principal Component Analysis | ||
| + | * UMAP | ||
| + | * Outlier Detection | ||
| + | - Other Forms of Learning | ||
| + | * Metric Learning | ||
| + | * Association Rule Learning | ||
| + | * Learning to Rank | ||
| + | * Learning to Recommend | ||
| + | * Factorization Machines | ||
| + | * Denoising Autoencoders | ||
| + | * Self-Supervised Learning: Word Embeddings | ||
| + | - Conclusion | ||
| + | * Topic Modeling | ||
| + | * Gaussian Processes | ||
| + | * Generalized Linear Models | ||
| + | * Probabilistic Graphical Models | ||
| + | * Markov Chain Monte Carlo | ||
| + | * Genetic Algorithms | ||
| + | * Reinforcement Learning | ||
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