00 | 16 Weeks
Enrollment Minimum Requirements
- Basic computer skills
- laptop
- Access to the internet
Course Contents/Details
DSM011: Supervised Learning - Linear Regression
Course Objectives
After completing this course, you will be able to:
• Understand the concepts of supervised learning, simple linear regression, and multiple linear
regression
• Handle and process raw, unclean data to get it ready for analysis and modeling using Python
• Apply linear regression on real-world data and identify factors that will help drive business
decisions
• Assess the model performance using di erent metrics
Prerequisites
•Participants are expected to have basic knowledge of mathematical concepts such as basic
calculus (derivatives) and descriptive statistics.
•Participants are expected to have a working knowledge of Python data structures (lists, tuples,
dictionaries), coding concepts (conditional statements, looping statements, list comprehensions),
and Python libraries such as pandas, numpy, matplotlib, and seaborn.
•Participants are expected to be comfortable with installing Python packages and reading the
Python documentation.
Topics Covered
• Introduction to Supervised Learning: Linear regression
• Introduction to learning from data
• Simple Linear Regression
• Multiple Linear Regression
• Evaluating a regression model
• Pros and Cons
• Hand-on Linear Regression
• Data Preprocessing
• Data Manipulations
• Dealing with Text Data
• Missing Value Treatment
• Feature Engineering Basics and Variable Transformation
• Variable Scaling
• Encoding Categorical Data
• Outlier Identification and Treatment
