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Machine Learning Quick Reference

You're reading from Machine Learning Quick Reference Quick and essential machine learning hacks for training smart data models

Product type Paperback

Published in Jan 2019

Publisher Packt

ISBN-13 9781788830577

Length 294 pages

Edition 1st Edition

Languages

Python

Tools

Scikit-learn

Concepts

Machine Learning

Author (1):

Kumar

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Table of Contents (13) Chapters

Preface

1. Quantifying Learning Algorithms FREE CHAPTER

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

12. Other Books You May Enjoy

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Summary

In this chapter, we studied Granger causality, which is where we use a single time series to predict its future values, along with the different models of graphical causality models. The graphical causality models cover two examples that will give us a basic idea about the graphical causal models.

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Authors (1)

Kumar

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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