The Hundred-Page Machine Learning Book

Foreword
Preface
	Who This Book is For
	How to Use This Book
	Should You Buy This Book?
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
Notation and Definitions
	Notation
		Data Structures
		Capital Sigma Notation
		Capital Pi Notation
		Operations on Sets
		Operations on Vectors
		Functions
		Max and Arg Max
		Assignment Operator
		Derivative and Gradient
	Random Variable
	Unbiased Estimators
	Bayes' Rule
	Parameter Estimation
	Parameters vs. Hyperparameters
	Classification vs. Regression
	Model-Based vs. Instance-Based Learning
	Shallow vs. Deep Learning
Fundamental Algorithms
	Linear Regression
		Problem Statement
		Solution
	Logistic Regression
		Problem Statement
		Solution
	Decision Tree Learning
		Problem Statement
		Solution
	Support Vector Machine
		Dealing with Noise
		Dealing with Inherent Non-Linearity
	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
		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
		Precision/Recall
		Accuracy
		Cost-Sensitive Accuracy
		Area under the ROC Curve (AUC)
	Hyperparameter Tuning
		Cross-Validation
Neural Networks and Deep Learning
	Neural Networks
		Multilayer Perceptron Example
		Feed-Forward Neural Network Architecture
	Deep Learning
		Convolutional Neural Network
		Recurrent Neural Network
Problems and Solutions
	Kernel Regression
	Multiclass Classification
	One-Class Classification
	Multi-Label Classification
	Ensemble Learning
		Boosting and Bagging
		Random Forest
		Gradient Boosting
	Learning to Label Sequences
	Sequence-to-Sequence Learning
	Active Learning
	Semi-Supervised Learning
	One-Shot Learning
	Zero-Shot Learning
Advanced Practice
	Handling Imbalanced Datasets
	Combining Models
	Training Neural Networks
	Advanced Regularization
	Handling Multiple Inputs
	Handling Multiple Outputs
	Transfer Learning
	Algorithmic Efficiency
Unsupervised Learning
	Density Estimation
	Clustering
		K-Means
		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
	Learning to Rank
	Learning to Recommend
		Factorization Machines
		Denoising Autoencoders
	Self-Supervised Learning: Word Embeddings
Conclusion
	What Wasn't Covered
		Topic Modeling
		Gaussian Processes
		Generalized Linear Models
		Probabilistic Graphical Models
		Markov Chain Monte Carlo
		Generative Adversarial Networks
		Genetic Algorithms
		Reinforcement Learning
	Acknowledgements
Index