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Clustering
Objectives:
Define clustering for ML applications.
Prepare data for clustering.
Define similarity for your dataset.
Compare manual and supervised similarity measures.
Use the k-means algorithm to cluster data.
Evaluate the quality of your clustering result.
The clustering self-study is an implementation-oriented introduction to clustering.
This course is not:
an exhaustive review of clustering
an exhaustive description of and comparison between different algorithmic approaches to clustering
a course on clustering with TensorFlow
a tutorial on classification (as opposed to clustering)
Prerequisites
This course assumes you have:
Completed Introduction to Machine Learning Problem Framing or have equivalent knowledge.
Completed Machine Learning Crash Course or have equivalent knowledge.
Completed Data Preparation and Feature Engineering or have equivalent knowledge.
Basic knowledge of data distributions, such as Gaussian and power law distributions.
Basic programming knowledge in Python.
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