Book Image

Mastering Machine Learning Algorithms. - Second Edition

By : Giuseppe Bonaccorso
Book Image

Mastering Machine Learning Algorithms. - Second Edition

By: Giuseppe Bonaccorso

Overview of this 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.
Table of Contents (28 chapters)
26
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27
Index

Further reading

  • Pratt J., Raiffa H., Schlaifer R., Introduction to Statistical Decision Theory, The MIT Press, 2008
  • Hoffmann M. D., Gelman A., The No-U-Turn Sampler: Adaptively Setting Path Lengths in Hamiltonian Monte Carlo, arXiv:1111.4246, 2011
  • A. Gelman, J. B. Carlin, H. S. Stern, Bayesian Data Analysis, CRC Press, 2013
  • Walsh B., Markov Chain Monte Carlo and Gibbs Sampling, Lecture Notes for EEB 596z, 2002
  • R. A. Howard, Dynamic Programming and Markov Process, The MIT Press, 1960
  • Rabiner L. R., A tutorial on hidden Markov models and selected applications in speech recognition, Proceedings of the IEEE 77.2, 1989
  • W. K. Hastings, Monte Carlo sampling methods using Markov chains and their applications, Biometrik, 57/1, 04/1970
  • Kevin B. Korb, Ann E. Nicholson, Bayesian Artificial Intelligence, CRC Press, 2010
  • Pearl J., Causality, Cambridge University Press, 2009
  • L. E. Baum, T. Petrie, Statistical Inference for Probabilistic Functions of Finite...