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Practical Machine Learning

You're reading from   Practical Machine Learning Learn how to build Machine Learning applications to solve real-world data analysis challenges with this Machine Learning book – packed with practical tutorials

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Product type Paperback
Published in Jan 2016
Publisher Packt
ISBN-13 9781784399689
Length 468 pages
Edition 1st Edition
Languages
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Author (1):
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Sunila Gollapudi Sunila Gollapudi
Author Profile Icon Sunila Gollapudi
Sunila Gollapudi
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Toc

Table of Contents (16) Chapters Close

Preface 1. Introduction to Machine learning 2. Machine learning and Large-scale datasets FREE CHAPTER 3. An Introduction to Hadoop's Architecture and Ecosystem 4. Machine Learning Tools, Libraries, and Frameworks 5. Decision Tree based learning 6. Instance and Kernel Methods Based Learning 7. Association Rules based learning 8. Clustering based learning 9. Bayesian learning 10. Regression based learning 11. Deep learning 12. Reinforcement learning 13. Ensemble learning 14. New generation data architectures for Machine learning Index

Implementing Decision trees

Refer to the source code provided for this chapter for implementing Decision Trees and Random Forests (source code path .../chapter5/... under each of the folder for the technology).

Using Mahout

Refer to the folder .../mahout/chapter5/decisiontreeexample/.

Refer to the folder.../mahout/chapter5/randomforestexample/.

Using R

Refer to the folder .../r/chapter5/decisiontreeexample/.

Refer to the folder .../r/chapter5/randomforestexample/.

Using Spark

Refer to the folder .../spark/chapter5/decisiontreeexample/.

Refer to the folder .../spark/chapter5/randomforestexample/.

Using Python (scikit-learn)

Refer to the folder .../python scikit-learn/chapter5/decisiontreeexample/.

Refer to the folder .../python scikit-learn/chapter5/randomforestexample/.

Using Julia

Refer to the folder .../julia/chapter5/decisiontreeexample/.

Refer to the folder .../julia/chapter5/randomforestexample/.

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