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Data Structures & AlgorithmsComplexity analysis up to graphs and DP.Operating SystemsProcesses, scheduling, memory and concurrency.Database Management SystemsRelational modelling, transactions and indexing.Computer NetworksFrom wires and radio waves up to the web.

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AI Inference EngineeringQuantization, batching and KV caching in production.System DesignScalable systems from first principles.Context EngineeringDesigning the context that LLMs actually reason over.Docker & KubernetesContainers and orchestration, explained.

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Machine Learning

Math Foundations4

Supervised Learning6

Training & Evaluation4

Unsupervised Learning & Ensembles3

Neural Networks4

Tools & Practice6

NumPy and pandas, the two libraries under everything
Visualizing data and models with Matplotlib
scikit-learn, every classical model in a few lines
PyTorch, from your hand-written network to a real framework
An end-to-end project walkthrough
Cheat sheet: NumPy, pandas, Matplotlib, scikit-learn, PyTorch

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Machine Learning

Math Foundations4

Supervised Learning6

Training & Evaluation4

Unsupervised Learning & Ensembles3

Neural Networks4

Tools & Practice6

NumPy and pandas, the two libraries under everything
Visualizing data and models with Matplotlib
scikit-learn, every classical model in a few lines
PyTorch, from your hand-written network to a real framework
An end-to-end project walkthrough
Cheat sheet: NumPy, pandas, Matplotlib, scikit-learn, PyTorch

Learn Machine Learning Tools & Practice

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An end-to-end project walkthrough

Every lesson in this course, applied in order, to one real dataset from raw CSV to a trained, honestly-evaluated model.

12 min readUpdated 2026-09-03machine-learningprojectscikit-learntools