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