Roadmap

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A 42-module path through AI and machine learning, released one module at a time. NumPy Foundations is available. Everything else is planned and marked upcoming.

If you want a Python checklist, a short readiness check, and local setup notes first, start at Start here.

1. Phase 1 — Data and mathematical foundations

   1. 1.NumPy Foundationsavailable
   2. 2.Data analysis with pandasupcoming
   3. 3.SQL and relational databasesupcoming
   4. 4.Exploratory data analysis and visualisationupcoming
   5. 5.Linear algebra for MLupcoming
   6. 6.Probability, statistics and calculus essentialsupcoming

2. Phase 2 — Supervised machine learning

   1. 7.ML workflow, baselines, data splits and leakageupcoming
   2. 8.Regression, gradient descent and regularisationupcoming
   3. 9.Classification, probabilities and thresholdsupcoming
   4. 10.KNN, Naive Bayes and SVMupcoming
   5. 11.Decision trees, random forests and boostingupcoming
   6. 12.Evaluation, cross-validation, imbalance, calibration, tuning and error analysisupcoming

3. Phase 3 — Broader ML and first deployed application

   1. 13.Clustering: K-Means, hierarchical clustering and DBSCANupcoming
   2. 14.Dimensionality reduction and anomaly detectionupcoming
   3. 15.Time-series forecastingupcoming
   4. 16.Recommendation and search foundationsupcoming
   5. 17.APIs and application backendsupcoming
   6. 18.End-to-end deployed ML projectupcoming

4. Phase 4 — Deep learning

   1. 19.Deep Learning Foundations, including a NumPy neural networkupcoming
   2. 20.A deep-learning frameworkupcoming
   3. 21.Training and debugging neural networksupcoming
   4. 22.Computer vision and transfer learningupcoming
   5. 23.Sequence models and NLP foundationsupcoming
   6. 24.Attention and transformersupcoming

5. Phase 5 — LLM application engineering

   1. 25.How LLMs workupcoming
   2. 26.Building with model APIsupcoming
   3. 27.Embeddings and semantic searchupcoming
   4. 28.Retrieval-augmented generationupcoming
   5. 29.AI application evaluation and securityupcoming
   6. 30.RAG application project and releaseupcoming

6. Phase 6 — Agents, adaptation and multimodal AI

   1. 31.Tool use and deterministic workflowsupcoming
   2. 32.Agent engineering, state, stopping conditions and permissionsupcoming
   3. 33.Fine-tuning and model adaptationupcoming
   4. 34.Inference efficiency and quantisationupcoming
   5. 35.Multimodal applicationsupcoming
   6. 36.Evaluated AI workflow projectupcoming

7. Phase 7 — Production engineering and capstone

   1. 37.Reproducibility, experiment tracking and model/data versioningupcoming
   2. 38.Deployment, Docker, CI/CD and cloud fundamentalsupcoming
   3. 39.Monitoring, quality, drift, reliability and costsupcoming
   4. 40.AI system designupcoming
   5. 41.Independent capstone implementationupcoming
   6. 42.Capstone defence and portfolioupcoming