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Julia for Data Science

You're reading from  Julia for Data Science

Product type Book
Published in Sep 2016
Publisher Packt
ISBN-13 9781785289699
Pages 346 pages
Edition 1st Edition
Languages
Author (1):
Anshul Joshi Anshul Joshi
Profile icon Anshul Joshi

Table of Contents (17) Chapters

Julia for Data Science
Credits
About the Author
About the Reviewer
www.PacktPub.com
Preface
1. The Groundwork – Julia's Environment 2. Data Munging 3. Data Exploration 4. Deep Dive into Inferential Statistics 5. Making Sense of Data Using Visualization 6. Supervised Machine Learning 7. Unsupervised Machine Learning 8. Creating Ensemble Models 9. Time Series 10. Collaborative Filtering and Recommendation System 11. Introduction to Deep Learning

What is machine learning?


Generally, when we talk about machine learning, we get into the idea of us fighting wars with intelligent machines that we created but went out of control. These machines are able to outsmart the human race and become a threat to human existence. These theories are just created for our entertainment. We are still very far away from such machines.

So, the question is: what is machine learning? Tom M. Mitchell gave a formal definition:

"A computer program is said to learn from experience E with respect to some class of tasks T and performance measure P if its performance at tasks in T, as measured by P, improves with experience E."

This implies that machine learning is teaching computers to generate algorithms using data without programming them explicitly. It transforms data into actionable knowledge. Machine learning has close association with statistics, probability, and mathematical optimization.

As technology grows, there is one thing that grows with it exponentially...

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