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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 FREE CHAPTER

2. Hierarchical Clustering

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

Interpreting t-SNE Plots

Now that we are able to use t-distributed SNE to visualize high-dimensional data, it is important to understand the limitations of such plots and what aspects are important in interpreting and generating them. In this section of the chapter, we will highlight some of the important features of t-SNE and demonstrate how care should be taken when using the visualization technique.

Perplexity

As described in the introduction to t-SNE, the perplexity values specify the number of nearest neighbors to be used in computing the conditional probability. The selection of this value can make a significant difference to the end result; with a low value of perplexity, local variations in the data dominate because a small number of samples are used in the calculation. Conversely, a large value of perplexity considers more global variations as many more samples are used in the calculation. Typically, it is worth trying a range of different values to investigate the effect of perplexity...

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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.

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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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