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Contents (hardcover)
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  * [[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