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)
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Further reading

  • Chapelle O., Schölkopf B., Zien A. (edited by), Semi-Supervised Learning, The MIT Press, 2010
  • Peters J., Janzing D., Schölkopf B., Elements of Causal Inference, The MIT Press, 2017
  • Howard R. A., Dynamic Programming and Markov Process, The MIT Press, 1960
  • Hughes G. F., On the mean accuracy of statistical pattern recognizers, IEEE Transactions on Information Theory, 14/1, 1968
  • Belkin M., Niyogi P., Semi-supervised learning on Riemannian manifolds, Machine Learning 56, 2004
  • Blum A., Mitchell T., Combining Labeled and Unlabeled Data with Co-Training, 11th Annual Conference on Computational Learning Theory, 1998
  • Loog M., Contrastive Pessimistic Likelihood Estimation for Semi-Supervised Classification, arXiv:1503.00269, 2015
  • Joachims T., Transductive Inference for Text Classification using Support Vector Machines, ICML Vol. 99/1999
  • Koller D., Friedman N., Probabilistic Graphical Models, The MIT Press, 2009
  • Bonaccorso G.,...