Course Description
Machine learning is a particular area of data science that uses techniques to create models from data without being explicitly programmed. In this course, you'll explore the conceptual elements of various machine learning techniques.
Learning Objectives
Machine Learning Introduction
- start the course
- identify problems in which supervised learning techniques apply
- identify problems in which unsupervised learning techniques apply
- apply linear regression to machine learning problems
- identify predictors in machine learning
Regression and Classification
- apply logistic regression to machine learning problems
- describe the use of dummy variables
- use naive bayes classification techniques
- work with decision trees
Clustering
- describe K-means clustering
- define cluster validation
- define principal component analysis
Errors and Validation
- describe machine learning errors
- describe underfitting
- describe overfitting
- apply k-folds cross validation
- describe fall-forward and back-propagation in neural networks
- describe SVMs and their use
Practice: Choosing a Method
- choose the appropriate machine learning method for the given example problems
Framework Connections
Specialty Areas
- Data Administration
- Systems Analysis
Feedback
If you would like to provide feedback for this course, please e-mail the NICCS SO at NICCS@hq.dhs.gov.