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68 topics · 98+ lessons on the flagship spine
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The language foundation for data work, ML scripts, and in-browser labs.
Linear algebra, probability, statistics, and calculus for ML.
Supervised & unsupervised learning, evaluation, and feature engineering.
Neural networks, backprop, optimization, and modern architectures.
Tensors, autograd, nn.Module, and the training loop you will actually write.
Images, CNNs, and visual models as one stage of the AI-engineer path — not the whole path.
Text pipelines, embeddings, and language tasks before you specialize in LLMs.
Zero-shot, chain-of-thought, structured outputs, and evaluation.
How modern LLMs work, and how to build around generation systems.
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