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

  • Alpaydin E., Introduction to Machine Learning, The MIT Press, 2010
  • Breiman L., Random Forests, Machine Learning, 45, 2001
  • Friedman J., Hastie T., Tibshirani R., Additive Logistic Regression: A Statistical View of Boosting, Annals of Statistics, 28/1998
  • Zhu J., Rosset S., Zou H., Hastie T., Multi-Class AdaBoost, Statistics, and Its Inference, 02/2009
  • Drucker H., Improving Regressors Using Boosting Techniques, ICML 1997
  • Starttech Educational Services LLP, Decision Trees, Random Forests, AdaBoost and XGBoost in Python, Packt Publishing, 2019
  • Lundberg S. M., Lee S., A Unified Approach to Interpreting Model Predictions, Advances in Neural Information Processing Systems 30, NIPS, 2017
  • Bonaccorso G., Machine Learning Algorithms Second Edition, Packt Publishing, 2018