A complete, project-driven path from “I know Python” to “I can build a machine learning model.” Students learn to clean and explore real data, understand the math underneath it, visualize what it’s telling them, and train models that make predictions — the same workflow professional data scientists use.
Core data science tools (NumPy and Pandas), the math foundations behind machine learning, data visualization, data wrangling on messy real-world datasets, and both supervised learning (regression, classification) and unsupervised learning (clustering, anomaly detection).
Multiple real capstone projects using real datasets — from customer behavior analysis to a clustering-based smart city project — giving students an actual portfolio, not just exercises.
Students should have completed Python for Kids: Part 1, 2, and 3. No prior statistics or math background is needed — it’s taught as part of the course.
Students curious about AI and machine learning who want to understand how it actually works, and students building a STEM portfolio for college applications.