3.2 Introduction to Machine Learning

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:

• Machine Learning (ML) foundation and concepts
  • Definition of ML
  • Types of ML
  • Types of Supervised Learning (Classification and Regression)
• Data Types
• Linear Regression
• Logistic Regression
• K-Nearest Neighbors (KNN)

Learners will be able to:

• Understand the definition and different types of ML
• Compare the different data types
• Train linear regression, logistic regression and KNN 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	Concept	Part 1: Introduction to machine learning.
	1 HR MARK
30 mins	Code-along	Part 2: Linear regression.
10 mins	Break
20 mins	Code-along	Part 3: Logistic regression.
	2 HR MARK
50 mins	Code-along	Part 4: KNN.
10 mins	Briefing / Q&A	Brief on references, assignment, quiz and Q&A.
	END CLASS 3 HR MARK