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Applied Unsupervised Learning with Python

You're reading from Applied Unsupervised Learning with Python Discover hidden patterns and relationships in unstructured data with Python

Product type Paperback

Published in May 2019

Publisher

ISBN-13 9781789952292

Length 482 pages

Edition 1st Edition

Languages

Python

Tools

Scikit-learn

Concepts

Machine Learning

Authors (3):

Benjamin Johnston

Christopher Kruger

Aaron Jones

View More author details

Table of Contents (12) Chapters

Applied Unsupervised Learning with Python

Preface

1. Introduction to Clustering

2. Hierarchical Clustering FREE CHAPTER

3. Neighborhood Approaches and DBSCAN

4. Dimension Reduction and PCA

5. Autoencoders

6. t-Distributed Stochastic Neighbor Embedding (t-SNE)

7. Topic Modeling

8. Market Basket Analysis

9. Hotspot Analysis

Appendix

Chapter 7. Topic Modeling

Note

Learning Objectives

By the end of this chapter, you'll be able to:

Perform basic cleaning techniques for textual data
Evaluate latent Dirichlet allocation models
Execute non-negative matrix factorization models
Interpret the results of topic models
Identify the best topic model for the given scenario

Note

In this chapter, we will see how topic modeling provides insights into the underlying structure of documents.

The rest of the chapter is locked

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Authors (3)

Benjamin Johnston

Benjamin Johnston

Benjamin Johnston is a senior data scientist for one of the world's leading data-driven MedTech companies and is involved in the development of innovative digital solutions throughout the entire product development pathway, from problem definition to solution research and development, through to final deployment. He is currently completing his Ph.D. in machine learning, specializing in image processing and deep convolutional neural networks. He has more than 10 years of experience in medical device design and development, working in a variety of technical roles, and holds first-class honors bachelor's degrees in both engineering and medical science from the University of Sydney, Australia.

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Christopher Kruger

Christopher Kruger

Christopher Kruger is a practicing data scientist and AI researcher. He has managed applied machine learning projects across multiple industries while mentoring junior team members on best practices. His primary focus is on pushing both business practicality as well as academic rigor in every project. Chris is currently developing research in the computer vision space.

See other products by Christopher Kruger

Aaron Jones

Aaron Jones

Aaron Jones is a full-time senior data scientist and consultant. He has built models and data products while working in retail, media, and environmental science. Aaron is based in Seattle, Washington and has a particular interest in clustering algorithms, natural language processing, and Bayesian statistics.

See other products by Aaron Jones

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