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LEARNPATH

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AI Engineer / Machine learning fundamentals

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AI Engineer

01Python syntax & types
02Lists, dicts, sets
03Linear algebra intuition
04Probability & statistics
05SQL fundamentals
06Data wrangling
07Machine learning fundamentals
08Neural networks
09PyTorch basics
10Evaluation & metrics
11MLOps basics
12Containers & orchestration
13AI ethics & responsibility

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Stage 02 · 02 · Data & classical ML · skill 3 of 3

Machine learning fundamentals

Supervised learning, features, loss, overfitting. Models are not magic.

Recommended first: Python syntax & types, Probability & statistics

After this you will be able to

Fit a baseline model
Evaluate with an appropriate metric
Diagnose overfitting vs underfitting

Open Intro to Machine Learning

Kaggle · why we picked it

Completing this unlocks: Neural networks, Evaluation & metrics, MLOps basics

Previous Next: Neural networks

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No playground attached to this skill. The practice lab has every runtime.

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