Domain Scientists

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1. Prompting & Using AI Well

   Stage 0 · 3 h · 3 h total

   Prompting techniques: role, context, examples, step-by-step thinking and structured output.

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2. Python, NumPy & Tooling

   Stage 1 · 12 h · 15 h total

   Read and write Python, vectorise with NumPy, use the command line, Git and Jupyter.

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3. Probability, Statistics & Information Theory

   Stage 1 · 12 h · 27 h total

   Distributions, expectation, Bayes, maximum likelihood, cross-entropy and KL divergence.

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4. PyTorch & Autograd

   Stage 1 · 10 h · 37 h total

   Tensors, autograd, data loading and the training loop.

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5. The ML Workflow: Data, Loss & Generalization

   Stage 2 · 10 h · 47 h total · lesson ready

   Train/validation/test splits, overfitting, bias–variance, cross-validation and metrics.

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6. Trees & Ensembles

   Stage 2 · 8 h · 55 h total

   Decision trees, random forests, gradient boosting, XGBoost/LightGBM — the kings of tabular data.

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7. Unsupervised Learning

   Stage 2 · 8 h · 63 h total

   k-means, Gaussian mixtures and EM, PCA, t-SNE/UMAP.

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8. Neural Networks & Backpropagation

   Stage 3 · 10 h · 73 h total · lesson ready

   From neurons and multilayer perceptrons to backpropagation and activation functions.

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9. Transformers & Attention

   Stage 4 · 16 h · 89 h total · lesson ready

   Self-attention, multi-head attention, positional encoding, residuals and normalisation — build a GPT from scratch.

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10. Instruction Tuning & PEFT

    Stage 5 · 10 h · 99 h total · lesson ready

    SFT, synthetic data, LoRA/QLoRA/DoRA, adapters and model merging.

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11. Vision Transformers & Self-Supervised Vision

    Stage 7 · 8 h · 107 h total

    ViT, SimCLR, MAE, JEPA and SAM.

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12. RAG Engineering & Evaluation

    Stage 8 · 8 h · 115 h total · lesson ready

    Chunking, query rewriting, HyDE, GraphRAG, citations and evaluation metrics.

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13. Agents: Tool Use, MCP, Planning & Memory

    Stage 8 · 12 h · 127 h total · lesson ready

    ReAct, function calling, MCP, reflection, memory, multi-agent systems, computer use and sandboxes.

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14. AI for Science & Domain Applications

    Stage 9 · 10 h · 137 h total

    Graph neural networks, AlphaFold, AlphaGeometry and automated research — linked with AIMap’s “AI × Disciplines” section.

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15. Tracking the Frontier: Reading & Reproducing Papers

    Stage 9 · 6 h · 143 h total

    The three-pass reading method, tracking arXiv and conferences, reproduction and writing.

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• 1Everyday Users · No code: in 2–3 weeks, learn to understand and use AI well and spot hallucinations and risks.40h
• 2Application Developers · Python is enough: in 3 months, ship a production-ready RAG / agent app you can evaluate and deploy.126h
• 3Algorithm Researchers · From maths to frontier papers: in 6–12 months, read and reproduce papers on LLMs, alignment and reasoning models.257h
• 4Product Managers & Leaders · Understand technical limits, cost structure and risk well enough to choose tools, start projects and set governance.84h