A practical, project-driven introduction to machine learning — covering the algorithms, tools and workflow used in real ML roles.
NumPy, pandas and the data manipulation essentials for machine learning work.
The core statistics and linear algebra concepts needed to understand ML algorithms.
Regression, classification, decision trees and ensemble methods using scikit-learn.
Clustering, dimensionality reduction and pattern discovery techniques.
Cross-validation, hyperparameter tuning and avoiding overfitting.
An end-to-end ML project — from raw data to a working, evaluated model.
Sit in on a live Machine Learning session, free of charge, and see whether it's the right fit.
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