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Hands-On Ensemble Learning with Python

You're reading from   Hands-On Ensemble Learning with Python Build highly optimized ensemble machine learning models using scikit-learn and Keras

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Product type Paperback
Published in Jul 2019
Publisher Packt
ISBN-13 9781789612851
Length 298 pages
Edition 1st Edition
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Authors (2):
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Konstantinos G. Margaritis Konstantinos G. Margaritis
Author Profile Icon Konstantinos G. Margaritis
Konstantinos G. Margaritis
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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George Kyriakides George Kyriakides
Author Profile Icon George Kyriakides
George Kyriakides
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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Toc

Table of Contents (20) Chapters Close

Preface 1. Section 1: Introduction and Required Software Tools FREE CHAPTER
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

Creating a model

The most important step in sentiment analysis (as is the case with most machine learning problems) is the preprocessing of our data. The following table contains 10 tweets, randomly sampled from the dataset:

id

text

44

@JonathanRKnight Awww I soo wish I was there to see...

143873

Shaking stomach flipping........god i hate thi...

466449

why do they refuse to put nice things in our v...

1035127

@KrisAllenmusic visit here

680337

Rafa out of Wimbledon Love Drunk by BLG out S...

31250

It's official, printers hate me Going to sul...

1078430

@_Enigma__ Good to hear

1436972

Dear Photoshop CS2. i love you. and i miss you!

401990

my boyfriend got in a car accident today !

1053169

Happy birthday, Wisconsin! 161 years ago, you ...
An outline of 10 random samples from the dataset

We can immediately...

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Authors (2)

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Profile icon Kyriakides
Kyriakides
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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See other products by Kyriakides
Profile icon Margaritis
Margaritis
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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