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Hands-On Automated Machine Learning

You're reading from Hands-On Automated Machine Learning A beginner's guide to building automated machine learning systems using AutoML and Python

Product type Paperback

Published in Apr 2018

Publisher Packt

ISBN-13 9781788629898

Length 282 pages

Edition 1st Edition

Languages

Python

Tools

Assemble

Concepts

Machine Learning

Authors (2):

Das

Mert Cakmak

View More author details

Table of Contents (15) Chapters

Title Page

Copyright and Credits

Packt Upsell

Contributors

Preface

1. Introduction to AutoML FREE CHAPTER

2. Introduction to Machine Learning Using Python

3. Data Preprocessing

4. Automated Algorithm Selection

5. Hyperparameter Optimization

6. Creating AutoML Pipelines

7. Dive into Deep Learning

8. Critical Aspects of ML and Data Science Projects

1. Other Books You May Enjoy

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Index

Clustering

We will begin this section with a question. How do we start learning a new algorithm or a machine learning method? We start with triple W. So, let's being with that for the clustering method.

What is clustering?

Clustering is a technique to group similar data together, and a group has some unique characteristics that are different from other groups. Data can be clustered together using various methods. One of them is rule-based, where the groups are formed based on certain predefined conditions, such as grouping customers based on their age or industry. Another method is to use ML algorithms to cluster data together.

Where is clustering used?

Being an unsupervised learning process, it is most often used in industries to deduce logical relationships and patterns from data. Clustering finds its application across sectors and business functions. It is used for information retrieval, customer segmentation, image segmentation, clustering unstructured text like web pages, news articles...

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Authors (2)

Das

Das

Sibanjan Das is a Business Analytics and Data Science consultant. He has extensive experience in implementing predictive analytics solutions in Business Systems and IoT. An enthusiastic and passionate professional about technology and innovation, he has the passion for wrangling with data since early days of his career. Sibanjan holds a Masters IT degree with major in Business Analytics from Singapore Management University and holds several industry certifications such as OCA, OCP and CSCMS.

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Mert Cakmak

Mert Cakmak

Umit Mert Cakmak is a data scientist at IBM, where he excels at helping clients solve complex data science problems, from inception to delivery of deployable assets. His research spans multiple disciplines beyond his industry and he likes sharing his insights at conferences, universities, and meet-ups.

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