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Contents (hardcover)
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Linear Regression
Logistic Regression
Decision Tree Learning
Support Vector Machine
k-Nearest Neighbors
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Building Blocks of a Learning Algorithm
Gradient Descent
How Machine Learning Engineers Work
Learning Algorithms' Particularities
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Feature Engineering
Learning Algorithm Selection
Three Sets
Underfitting and Overfitting
Regularization
Model Performance Assessment
Hyperparameter Tuning
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Neural Networks
Deep Learning
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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
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Density Estimation
Clustering
Dimensionality Reduction
Outlier Detection
Other Forms of Learning
Conclusion