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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 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

Julia


Julia, in recent times, has gained much popularity and adoption in the Machine learning and data science fields as a high-performance alternative to Python. Julia is a dynamic programming language that is built to support distributed and parallel computing, thus known to be convenient and fast.

Performance in Julia is a result of the JIT compiler and type interfacing feature. Also, unlike other numeric programming languages, Julia does not enforce vectorization of values. Similar to R, MATLAB, and Python, Julia provides ease and expressiveness for high-level numerical computing.

Following are some key characteristics of Julia:

  • The core APIs and mathematical primitive operations are written in Julia

  • It consists rich types for constructing and describing objects

  • Julia supports for multiple dispatch that enable using functions across many combinations of arguments

  • It facilitates the automation of specialized code generation for different argument types

  • Proven performance is on par with statically...

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