3.4 Supervised Learning Advanced

Dependencies

Refer to the following markdown file for the respective sections of the class:

• Self Studies
• Lesson
• Assignment
• Quiz
• Reference

Lesson Objectives

Learners will understand:

• Hyperparameters evaluation and tuning
• Baggings and boosting
• Advanced supervised learning models such as decision trees, random forests, and gradient boosting

Learners will be able to:

• Evaluate and tune hyperparameters
• Build end-to-end supervised learning workflow with advanced supervised learning models

Lesson Plan

Duration	What	How or Why
- 5mins	Start zoom session	So that learners can join early and start class on time.
20 mins	Activity	Recap on self-study and prework materials.
40 mins	Code-along	Part 1: Regularization, Hyperparameters Tuning, Cross Validation and Grid Search.
	1 HR MARK
30 mins	Code-along	Part 2: Model training workflow.
10 mins	Break
20 mins	Code-along	Part 3: Decision Trees.
	2 HR MARK
50 mins	Code-along	Part 4: Bagging vs Boosting, Random Forest, Gradient Boosting, LightGBM.
10 mins	Briefing / Q&A	Brief on references, assignment, quiz and Q&A.
	END CLASS 3 HR MARK