Mastering machine learning algorithms : expert techniques for implementing popular machine learning algorithms, fine-tuning your models, and understanding how they work Guiseppe Bonaccorso

Include index

Chapter 1. Machine Learning Model Fundamentals -- Chapter 2. Loss functions and Regularization -- Chapter 3. Introduction to Semi-Supervised Learning -- Chapter 4. Advanced Semi-Supervised Classifiation -- Chapter 5. Graph-based Semi-Supervised Learning -- Chapter 6. Clustering and Unsupervised Models -- Chapter 7. Advanced Clustering and Unsupervised Models -- Chapter 8. Clustering and Unsupervised Models for Marketing -- Chapter 9. Generalized Linear Models and Regression -- Chapter 10. Introduction to Time-Series Analysis -- Chapter 11. Bayesian Networks and Hidden Markov Models -- Chapter 12. The EM Algorithm -- Chapter 13. Component Analysis and Dimensionality Reduction -- Chapter 14. Hebbian Learning -- Chapter 15. Fundamentals of Ensemble Learning -- Chapter 16. Advanced Boosting Algorithms -- Chapter 17. Modeling Neural Networks -- Chapter 18. Optimizing Neural Networks -- Chapter 19. Deep Convolutional Networks -- Chapter 20. Recurrent Neural Networks -- Chapter 21. Auto-Encoders -- Chpater 22. Introduction to Generative Adversarial Networks -- Chapter 23. Deep Belief Networks -- Chapter 24. Introduction to Reinforcement Learning -- Chapter 25. Advanced Policy Estimation Algorithms.

A new second edition of the bestselling guide to exploring and mastering the most important algorithms for solving complex machine learning problems, updated to include Python 3.8 and TensorFlow 2.x as well as the latest in new algorithms and techniques.