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Machine Learning Quick Reference

You're reading from Machine Learning Quick Reference

Product type Book

Published in Jan 2019

Publisher Packt

ISBN-13 9781788830577

Pages 294 pages

Edition 1st Edition

Languages

Concepts

Machine Learning

Author (1):

Rahul Kumar

Table of Contents (18) Chapters

Title Page

Copyright and Credits

About Packt

Contributors

Preface

1. Quantifying Learning Algorithms

2. Evaluating Kernel Learning

3. Performance in Ensemble Learning

4. Training Neural Networks

5. Time Series Analysis

6. Natural Language Processing

7. Temporal and Sequential Pattern Discovery

8. Probabilistic Graphical Models

9. Selected Topics in Deep Learning

10. Causal Inference

11. Advanced Methods

1. Other Books You May Enjoy

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Index

Statistical models

A statistical model is the approximation of the truth that has been captured through data and mathematics or statistics, and acts as an enabler here. This approximation is used to predict an event. A statistical model is nothing but a mathematical equation.

For example, let's say we reach out to a bank for a home loan. What does the bank ask us? The first thing they would ask us to do is furnish lots of documents such as salary slips, identity proof documents, documents regarding the house we are going to purchase, a utility bill, the number of current loans we have, the number of dependants we have, and so on. All of these documents are nothing but the data that the bank would use to assess and check our creditworthiness:

What this means is that your creditworthiness is a function of the salary, number of loans, number of dependants, and so on. We can arrive at this equation or relationship mathematically.

Note

A statistical model is a mathematical equation that arrives at using given data for a particular business scenario.

In the next section, we will see how models learn and how the model can keep getting better.

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Machine Learning Quick Reference

Published in: Jan 2019 Publisher: Packt ISBN-13: 9781788830577

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Authors (1)

Rahul Kumar

Rahul Kumar has got more than 10 years of experience in the space of Data Science and Artificial Intelligence. His expertise lies in the machine learning and deep learning arena. He is known to be a seasoned professional in the area of Business Consulting and Business Problem Solving, fuelled by his proficiency in machine learning and deep learning. He has been associated with organizations such as Mercedes-Benz Research and Development (India), Fidelity Investments, Royal Bank of Scotland among others. He has accumulated a diverse exposure through industries like BFSI, telecom and automobile. Rahul has also got papers published in IIM and IISc Journals.

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