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Association Rule Learning
Foreword
Introduction
Notation and Definitions
Fundamental Algorithms
Linear Regression
Logistic Regression
Decision Tree Learning
Support Vector Machine
k-Nearest Neighbors
Anatomy of a Learning Algorithm
Building Blocks of a Learning Algorithm
Gradient Descent
How Machine Learning Engineers Work
Learning Algorithms' Particularities
Basic Practice
Feature Engineering
Learning Algorithm Selection
Three Sets
Underfitting and Overfitting
Regularization
Model Performance Assessment
Hyperparameter Tuning
Neural Networks and Deep Learning
Neural Networks
Deep Learning
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
Advanced Practice
Unsupervised Learning
Density Estimation
Clustering
Dimensionality Reduction
Outlier Detection
Other Forms of Learning
Conclusion