• Online, Self-Paced
Course Description

Discover how to implement neural network with data sampling and workflow models using scikit-learn, and explore the pre and post model approaches of implementing machine learning workflows.

Learning Objectives

AI and ML Solutions with Python: Deep Learning and Neural Network Implementation

  • implement recurrent neural network
  • work with data sampling
  • implement dimensionality reduction with PCA
  • demonstrate how to use the Gaussian processes for regression
  • describe the core concepts and features of Linear model
  • identify the pre-model and post-model workflow in analytics
  • work with Classification and Bayesian Ridge regression using scikit-learn
  • describe the core concept of Linear Regression model
  • demonstrate how to implement Logistic regression using linear methods
  • create and fit linear regression on a dataset and get the feature coefficient

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.

Feedback

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