Book Image

Machine Learning Quick Reference

By : Rahul Kumar
Book Image

Machine Learning Quick Reference

By: Rahul Kumar

Overview of this book

Machine learning makes it possible to learn about the unknowns and gain hidden insights into your datasets by mastering many tools and techniques. This book guides you to do just that in a very compact manner. After giving a quick overview of what machine learning is all about, Machine Learning Quick Reference jumps right into its core algorithms and demonstrates how they can be applied to real-world scenarios. From model evaluation to optimizing their performance, this book will introduce you to the best practices in machine learning. Furthermore, you will also look at the more advanced aspects such as training neural networks and work with different kinds of data, such as text, time-series, and sequential data. Advanced methods and techniques such as causal inference, deep Gaussian processes, and more are also covered. By the end of this book, you will be able to train fast, accurate machine learning models at your fingertips, which you can easily use as a point of reference.
Table of Contents (18 chapters)
Title Page
Copyright and Credits
About Packt
Contributors
Preface
Index

Contributors

About the author

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.

 

 

About the reviewers

Chiheb Chebbi is a Tunisian infosec enthusiast, author, and technical reviewer with experience in various aspects of information security, focusing on investigations into advanced cyber attacks and researching cyber espionage. His core interests lie in penetration testing, machine learning, and threat hunting. He has been included in many halls of fame. The proposals he has put forward with a view to giving presentations have been accepted by many world-class information security conferences.

 

I dedicate this book to every person who makes the security community awesome and fun!

 

DatTran is currently co-heading the data team at idealo.de, where he leads a team of data scientists and data engineers. His focus is to turn idealo into a machine learning powerhouse. His research interests range from traditional machine learning to deep learning. Previously, he worked for Pivotal Labs and Accenture. He is a regular public speaker and has presented at the PyData and Cloud Foundry summits. He also blogs about his work on Medium. His background is in operations research and econometrics. He received his MSc in Economics from Humboldt University, Berlin.

 

 

 

 

 

 

 

 

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