Course Description
Discover how to implement data classification using various techniques, including Bayesian, and learn to apply various search implementations with Python and scikit-learn.
Learning Objectives
AI and ML Solutions with Python: Implementing ML Algorithm Using scikit-learn
- work with least absolute shrinkage and selection operator
- demonstrate how to apply Bayesian Ridge regression using scikit-learn
- describe data classification using scikit-learn
- implement classifications with decision trees using scikit-learn
- demonstrate how to work with data classification using vector machines in scikit-learn
- demonstrate how to classify documents with Naive Bayes using scikit-learn
- work with Post model validation using the Cross model algorithm
- demonstrate how to work with cross model implementation using Shufflesplit
- implement poor man's grid search and brute force grid search
- create labels and features to classify data into train and test datasets and apply decision tree classifiers