You're reading from Mastering Machine Learning Algorithms Expert techniques for implementing popular machine learning algorithms, fine-tuning your models, and understanding how they work

Product type Paperback

Published in Jan 2020

Publisher Packt

ISBN-13 9781838820299

Length 798 pages

Edition 2nd Edition

Languages

Python

Tools

Keras

Concepts

Machine Learning

Authors (2):

Giuseppe Bonaccorso

View More author details

Table of Contents (28) Chapters

Preface

1. Machine Learning Model Fundamentals

2. Loss Functions and Regularization FREE CHAPTER

3. Introduction to Semi-Supervised Learning

4. Advanced Semi-Supervised Classification

5. Graph-Based Semi-Supervised Learning

6. Clustering and Unsupervised Models

7. Advanced Clustering and Unsupervised Models

8. Clustering and Unsupervised Models for Marketing

9. Generalized Linear Models and Regression

10. Introduction to Time-Series Analysis

11. Bayesian Networks and Hidden Markov Models

12. The EM Algorithm

13. Component Analysis and Dimensionality Reduction

14. Hebbian Learning

15. Fundamentals of Ensemble Learning

16. Advanced Boosting Algorithms

17. Modeling Neural Networks

18. Optimizing Neural Networks

19. Deep Convolutional Networks

20. Recurrent Neural Networks

21. Autoencoders

22. Introduction to Generative Adversarial Networks

23. Deep Belief Networks

24. Introduction to Reinforcement Learning

25. Advanced Policy Estimation Algorithms

26. Other Books You May Enjoy

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...