• Online, Self-Paced

Discover how to implement various supervised and unsupervised algorithms of machine learning using Python, with the primary focus of clustering and classification.

Learning Objectives

AI and ML Solutions with Python: Supervised, Unsupervised and Deep Learning

  • demonstrate how to implement classification
  • list the various types of algorithms used in unsupervised learning
  • demonstrate how to implement K-Mean clustering
  • demonstrate how to implement hierarchical clustering
  • demonstrate how to facilitate text mining and work with recommender systems
  • demonstrate the process involved in text mining and data assembly
  • specify the concepts of deep and reinforcement learning
  • work with Restricted Boltzmann machines
  • build models using Convolution Neural Network
  • utilize data frames and centroids

Framework Connections

The materials within this course focus on the NICE Framework Task, Knowledge, and Skill statements identified within the indicated NICE Framework component(s):

Specialty Areas

  • Software Development

Specialty Areas have been removed from the NICE Framework. With the recent release of the new NICE Framework data, updates to courses are underway. Until this course can be updated, this historical information is provided to give better context as to how it can help you with your cybersecurity goals.

Feedback

If you would like to provide feedback for this course, please e-mail the NICCS SO at NICCS@hq.dhs.gov.