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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Index

Time-series

In this chapter, we are going to briefly introduce the concepts of time-series and stochastic processes. As the topic is very wide, we are only discussing a few fundamental aspects and, at the same time, inviting the reader to refer to a complete book, such as Shumway R. H., Stoffer D. S., Time Series Analysis and Its Applications, Springer, 2017, for all the details.

The main concept of this chapter concerns the structure of a time-series. In this book, we are assuming that we are working with univariate series in the form:

Every value yi depends implicitly on time (that is, yi = y(i)); therefore, the series cannot be shuffled without losing information. If the values yt are completely determined by a law (such as yt = t2), the underlying process is described as deterministic. This is the case with many physical laws, but it's almost useless for us because the future can not be predicted without uncertainty. On the other hand, if every yi is a...