Building Unsupervised Learning Models with TensorFlow

This course is a part of Building Machine Learning Solutions with Tensorflow, a 11-course Paths series from Pluralsight.

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.