Machine Learning Foundations with Python

From data to a working model: the core ideas of machine learning, built step by step in Python.

Clean a real dataset, train your first models with scikit-learn, and learn to tell a good model from a lucky one. Designed for developers and students starting in data science.

What you will be able to do

• Prepare and explore a dataset
• Train and compare classification and regression models
• Evaluate models with the right metrics

Before you start

• Python basics: lists, functions, loops

Course outline

1. Explore and clean a dataset: A pandas notebook that finds and fixes data problems. A clean dataset and a short data report.
2. Your first models: Classification and regression models with scikit-learn. A baseline to beat.
3. Good model or lucky model?: Train and test splits, cross-validation, and the right metrics. An evaluation you can defend.
4. Features that matter: Feature engineering and a full pipeline. A reusable pipeline for your portfolio.

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