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)
1.3
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
create labels and features to classify data into train and test datasets and apply decision tree classifiers
Course Number: it_sdpyai_04_enus
Expertise Level
Intermediate