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Hands-On Ensemble Learning with Python

You're reading from  Hands-On Ensemble Learning with Python

Product type Book
Published in Jul 2019
Publisher Packt
ISBN-13 9781789612851
Pages 298 pages
Edition 1st Edition
Languages
Concepts
Authors (2):
George Kyriakides George Kyriakides
Profile icon George Kyriakides
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George Kyriakides is a Ph.D. researcher, studying distributed neural architecture search. His interests and experience include automated generation and optimization of predictive models for a wide array of applications, such as image recognition, time series analysis, and financial applications. He holds an M.Sc. in computational methods and applications, and a B.Sc. in applied informatics, both from the University of Macedonia, Thessaloniki, Greece.
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Konstantinos G. Margaritis Konstantinos G. Margaritis
Profile icon Konstantinos G. Margaritis
LinkedIn
Konstantinos G. Margaritis has been a teacher and researcher in computer science for more than 30 years. His research interests include parallel and distributed computing as well as computational intelligence and machine learning. He holds an M.Eng. in electrical engineering (Aristotle University of Thessaloniki, Greece), as well as an M.Sc. and a Ph.D. in computer science (Loughborough University, UK). He is a professor at the Department of Applied Informatics, University of Macedonia, Thessaloniki, Greece.
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Toc

Table of Contents (20) Chapters close

Preface 1. Section 1: Introduction and Required Software Tools
2. A Machine Learning Refresher 3. Getting Started with Ensemble Learning 4. Section 2: Non-Generative Methods
5. Voting 6. Stacking 7. Section 3: Generative Methods
8. Bagging 9. Boosting 10. Random Forests 11. Section 4: Clustering
12. Clustering 13. Section 5: Real World Applications
14. Classifying Fraudulent Transactions 15. Predicting Bitcoin Prices 16. Evaluating Sentiment on Twitter 17. Recommending Movies with Keras 18. Clustering World Happiness 19. Another Book You May Enjoy

Technical requirements

You will require basic knowledge of machine learning techniques and algorithms. Furthermore, a knowledge of python conventions and syntax is required. Finally, familiarity with the NumPy library will greatly help the reader to understand some custom algorithm implementations.

The code files of this chapter can be found on GitHub:

https://github.com/PacktPublishing/Hands-On-Ensemble-Learning-with-Python/tree/master/Chapter03

Check out the following video to see the Code in Action: http://bit.ly/2M52VY7.

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Authors (2)

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Profile icon George Kyriakides
LinkedIn
George Kyriakides is a Ph.D. researcher, studying distributed neural architecture search. His interests and experience include automated generation and optimization of predictive models for a wide array of applications, such as image recognition, time series analysis, and financial applications. He holds an M.Sc. in computational methods and applications, and a B.Sc. in applied informatics, both from the University of Macedonia, Thessaloniki, Greece.
Read more
See other products by George Kyriakides
Profile icon Konstantinos G. Margaritis
LinkedIn
Konstantinos G. Margaritis has been a teacher and researcher in computer science for more than 30 years. His research interests include parallel and distributed computing as well as computational intelligence and machine learning. He holds an M.Eng. in electrical engineering (Aristotle University of Thessaloniki, Greece), as well as an M.Sc. and a Ph.D. in computer science (Loughborough University, UK). He is a professor at the Department of Applied Informatics, University of Macedonia, Thessaloniki, Greece.
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