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Mastering Machine Learning Algorithms. Expert techniques for implementing popular machine learning algorithms, fine-tuning your models, and understanding how they work - Second Edition

Mastering Machine Learning Algorithms. Expert techniques for implementing popular machine learning algorithms, fine-tuning your models, and understanding how they work - Second Edition

Giuseppe Bonaccorso

E-book
Mastering Machine Learning Algorithms, Second Edition helps you harness the real power of machine learning algorithms in order to implement smarter ways of meeting today's overwhelming data needs. This newly updated and revised guide will help you master algorithms used widely in semi-supervised learning, reinforcement learning, supervised learning, and unsupervised learning domains.

You will use all the modern libraries from the Python ecosystem – including NumPy and Keras – to extract features from varied complexities of data. Ranging from Bayesian models to the Markov chain Monte Carlo algorithm to Hidden Markov models, this machine learning book teaches you how to extract features from your dataset, perform complex dimensionality reduction, and train supervised and semi-supervised models by making use of Python-based libraries such as scikit-learn. You will also discover practical applications for complex techniques such as maximum likelihood estimation, Hebbian learning, and ensemble learning, and how to use TensorFlow 2.x to train effective deep neural networks.

By the end of this book, you will be ready to implement and solve end-to-end machine learning problems and use case scenarios.
  • 1. Machine Learning Model Fundamentals
  • 2. Loss functions and Regularization
  • 3. Introduction to Semi-Supervised Learning
  • 4. Advanced Semi-Supervised Classifiation
  • 5. Graph-based Semi-Supervised Learning
  • 6. Clustering and Unsupervised Models
  • 7. Advanced Clustering and Unsupervised Models
  • 8. Clustering and Unsupervised Models for Marketing
  • 9. Generalized Linear Models and Regression
  • 10. Introduction to Time-Series Analysis
  • 11. Bayesian Networks and Hidden Markov Models
  • 12. The EM Algorithm
  • 13. Component Analysis and Dimensionality Reduction
  • 14. Hebbian Learning
  • 15. Fundamentals of Ensemble Learning
  • 16. Advanced Boosting Algorithms
  • 17. Modeling Neural Networks
  • 18. Optimizing Neural Networks
  • 19. Deep Convolutional Networks
  • 20. Recurrent Neural Networks
  • 21. Auto-Encoders
  • 22. Introduction to Generative Adversarial Networks
  • 23. Deep Belief Networks
  • 24. Introduction to Reinforcement Learning
  • 25. Advanced Policy Estimation Algorithms
  • Titel: Mastering Machine Learning Algorithms. Expert techniques for implementing popular machine learning algorithms, fine-tuning your models, and understanding how they work - Second Edition
  • Autor: Giuseppe Bonaccorso
  • Originaler Titel: Mastering Machine Learning Algorithms. Expert techniques for implementing popular machine learning algorithms, fine-tuning your models, and understanding how they work - Second Edition
  • ISBN: 9781838821913, 9781838821913
  • Veröffentlichungsdatum: 2020-01-31
  • Format: E-book
  • Artikelkennung: e_2aaz
  • Verleger: Packt Publishing