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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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Table of Contents (16) Chapters Close

Preface 1. Introduction to Machine learning FREE CHAPTER 2. Machine learning and Large-scale datasets 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

Machine learning tools – A landscape

There are several open source and commercial Machine learning frameworks and tools in the market that have evolved over the last few decades. While the field of Machine learning itself is evolving in building powerful algorithms for diverse requirements across domains, we now see a surge of open source options for large-scale Machine learning that have reached a significant level of maturity and are being widely adopted by the data science and Machine learning communities.

The model has changed significantly in the recent past, and researchers are encouraged to publish their software under an open source model. Since there are problems that authors face while publishing their work in using algorithmic implementations for Machine learning, any work that is reviewed and improvised through usage by the data science community is considered to be of more value.

The following figure shows a concept model of some important commercial and open source Machine...

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