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 concept of time-series and discussed the properties of stationary processes and how to manipulate a dataset to remove irregularities through a process called smoothing. This method allows us to perform a data cleansing step when the time-series is heavily affected by white noise. It's also helpful when it's important to visualize the trends or seasonality without the secondary effects due to the noise. We have shown how AR, MA, and ARMA models can successfully forecast stationary time-series and how, using the technique of differencing, it's possible to train ARIMA models in order to also forecast non-stationary time-series. Another fundamental concept that we have discussed is auto-correlation, which allows us to have a clear insight into the behavior of the time-series with minimal effort. This kind of analysis helps the data scientist to choose the most appropriate model.

In the next chapter, we start discussing...