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

  • Dempster A. P., Laird N. M., Rubin D. B., Maximum likelihood from incomplete data via the EM algorithm, Journal of the Royal Statistical Society, B, 39(1):1–38, 11/1977
  • Hansen F., Pedersen G. K. Jensen's Operator Inequality, arXiv:math/0204049 [math.OA]
  • Rubin D., Thayer D., EM algorithms for ML factor analysis, Psychometrika, 47/1982, Issue 1
  • Ghahramani Z., Hinton G. E., The EM algorithm for Mixtures of Factor Analyzers, CRC-TG-96-1, 05/1996
  • Hyvarinen A., Oja E., Independent Component Analysis: Algorithms and Applications, Neural Networks 13/2000
  • Luenberger D. G., Optimization by Vector Space Methods, Wiley, 1997
  • Ledoit O., Wolf M., A Well-Conditioned Estimator for Large-Dimensional Covariance Matrices, Journal of Multivariate Analysis, 88, 2/2004
  • Minka T.P., Automatic Choice of Dimensionality for PCA, NIPS 2000
  • Nasios N., Bors A. G., Variational Learning for Gaussian Mixture Models, IEEE Transactions on Systems...