Introduction to Machine Learning and AI

Overview

This intermediate-level course is designed for students aged 15+ who already have a good understanding of Python programming. Students will explore the foundational concepts of Machine Learning and Artificial Intelligence, including supervised and unsupervised learning, training and testing models, features and target variables, and model evaluation. Through practical work with Python and real datasets, learners will experiment with key ML models such as linear regression, decision trees, and clustering, developing an understanding of how machines learn from data and make predictions.

What you will build

You will build and train your own simple Machine Learning models using Python and real datasets. Through practical projects, you will create predictive models and explore how different approaches can be used to find patterns and make decisions from data.

What you will learn

• Fundamentals of probability theory and mathematical statistics
• Basic concepts and terminology of machine learning and AI
• Supervised and unsupervised learning, including regression, classification, and clustering
• Key machine learning models, such as linear regression, decision trees, and clustering algorithms
• Training, testing, evaluating, and improving machine learning models

What you need to know first

Students should be comfortable with Python programming, including variables, data types, conditions, loops, functions, and basic work with lists and data structures.