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

Summary

In this chapter, we have introduced the reader to the most important concepts concerning linear models and regressions. In the first part, we discussed the features of a GLM, focusing attention on how to fit the models and how to avoid the most common problems.

We also analyzed how to include regularization penalties through ridge and lasso regressions and how it's possible to exploit the linear framework also when the dataset is non-linear through an appropriate polynomial transformation. We also compared the results with the one obtained using an isotonic regression and we analyzed the reasons for preferring either the former or the latter. Another important topic discussed in the chapter is risk modeling using logistic regression penalized with lasso to perform an automatic feature selection.

In the next chapter, we start discussing the basic concepts and of time-series analysis, focusing on the properties on the most important models (ARMA and ARIMA) that are...