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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):

Umit Mert Cakmak

Sibanjan Das

View More author details

Table of Contents (10) Chapters

Preface

1. Introduction to AutoML

2. Introduction to Machine Learning Using Python FREE CHAPTER

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

9. Other Books You May Enjoy

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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.

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Hands-On Automated Machine Learning

Published in: Apr 2018

Publisher: Packt

ISBN-13: 9781788629898

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Authors (2)

Das

Das

Akhil Das is a senior software developer at Sigmoid primarily focusing on distributed computing, real-time analytics, performance optimization, and application scaling problems using a wide variety of technologies such as Apache Spark and Mesos, among others. He contributes actively to the Apache Spark project and is a regular speaker at big data conferences and meetups, MesosCon 2015 being the most recent one.

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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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