• Online, Self-Paced
Course Description

Discover how to differentiate between supervised and unsupervised machine learning techniques. The construction of clustering models and their application to classification problems is also covered.

Learning Objectives

Tensorflow: K-means Clustering with TensorFlow

  • Course Overview
  • distinguish between supervised and unsupervised learning algorithms
  • identify the characteristics of supervised learning algorithms
  • identify the characteristics of unsupervised learning algorithms
  • recognize use cases where unsupervised learning can be applied
  • define the objectives of clustering algorithms
  • describe the process of k-means clustering to group data
  • describe how to implement k-means clustering
  • recall how to install TensorFlow and work with Jupyter notebooks
  • generate random data for clustering algorithms
  • perform k-means clustering using a TensorFlow estimator
  • explore the Iris dataset of flowers
  • perform clustering and classification on the Iris dataset
  • recall characteristics of unsupervised learning algorithms
  • describe the process and use cases of clustering


Framework Connections

The materials within this course focus on the Knowledge Skills and Abilities (KSAs) identified within the Specialty Areas listed below. Click to view Specialty Area details within the interactive National Cybersecurity Workforce Framework.