ǂThe ǂhundred-page machine learning book

All you need to know about Machine Learning in a hundred pages Supervised and unsupervised learning, support vector machines, neural networks, ensemble methods, gradient descent, cluster analysis and dimensionality reduction, autoencoders and transfer learning, feature engineering and hyperparameter tuning! Math, intuition, illustrations, all in just a hundred pages! 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 The breadth of topics the book covers is amazing for just 100 pages (plus few bonus pages!). Burkov doesn't hesitate to go into the math equations: that's one thing that short books usually drop. I really liked how the author explains the core concepts in just a few words. The book can be very useful for newcomers in the field, as well as for old-timers who can gain from such a broad view of the field Burkov has undertaken a very useful but impossibly hard task in reducing all of machine learning to 100 pages. He succeeds well in choosing the topics - both theory and practice - that will be useful to practitioners, and for the reader who understands that this is the first 100 (or actually 150) pages you will read, not the last, provides a solid introduction to the field Provides a practical guide to get started and execute on machine learning within a few days without necessarily knowing much about machine learning. The first five chapters are enough to get you started and the next few chapters provide you a good feel of more advanced topics to pursue