AI and ML Solutions with Python: Implementing ML Algorithm Using scikit-learn


Overview/Description
Expected Duration
Lesson Objectives
Course Number
Expertise Level



Overview/Description

Discover how to implement data classification using various techniques, including Bayesian, and learn to apply various search implementations with Python and scikit-learn.



Expected Duration (hours)
1.2

Lesson 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
  • Course Number:
    it_sdpyai_04_enus

    Expertise Level
    Intermediate