Building Your First Machine Learning Model with Python - Session 2

Session 2: Building Your First Machine Learning Model with Python

In this hands-on session, we will build upon the foundations from Session 1 and guide participants step by step through the process of creating a simple machine learning model using scikit-learn. We will cover model selection, training, and evaluation using basic metrics, with a focus on understanding the key concepts behind supervised learning. Participants will leave with a working model and a clear idea of how machine learning can be applied in practical contexts.

Key learning outcomes
• Understand the basic workflow of a machine learning project
• Build and train a simple classification or regression model
• Evaluate model performance using key metrics
• Discuss real-world use cases and next steps for deeper learning

Level and prerequisites
This session is intended for participants with basic familiarity with Python and an interest in applying machine learning to real-world data. It is suitable for anyone who has attended Session 1 or has equivalent experience.

Software and setup
Participants will continue using the online Jupyter Notebook environment. No installation is required. The environment includes all necessary libraries such as pandas, matplotlib, and scikit-learn.

Instructor: Muniba Talha

Online. Direct link will be sent on the day of the webinar