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Building Unsupervised Learning Models with TensorFlow

by Janani Ravi

Unsupervised learning techniques work with huge data sets to find patterns within the data. This course teaches you the details of clustering and autoencoding, two versatile unsupervised learning techniques, and how to implement them in TensorFlow.

What you'll learn

Unsupervised learning techniques are powerful, but under utilized and often not well understood. In this course, Building Unsupervised Learning Models with TensorFlow, you'll learn the various characteristics and features of clustering models such as K-means clustering and hierarchical clustering.

First, you'll dive into building a k-means clustering model in TensorFlow.
Next, you'll discover autoencoders in detail, which are a type of artificial neural network used for unsupervised learning.
Finally, you'll explore encodings or representation of data for dimensionality reduction of problems.

By the end of this course, you'll have a better understanding of how you can work with unlabeled data using unsupervised learning techniques.

Table of contents

Course Overview

About the author

Janani has a Masters degree from Stanford and worked for 7+ years at Google. She was one of the original engineers on Google Docs and holds 4 patents for its real-time collaborative editing framework. After spending years working in tech in the Bay Area, New York, and Singapore at companies such as Microsoft, Google, and Flipkart, Janani finally decided to combine her love for technology with her passion for teaching. She is now the co-founder of Loonycorn, a content studio focused on providing ... more

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