ML & AI Engineering
From Python basics to production ML and AI systems
14 phases. 92 lessons. 92 labs. 6 projects. Python through SQL, classical ML, deep learning, transformers, LLM engineering, RAG, agents, evaluation, deployment, and cloud. You build real systems, deploy them, and graduate with a portfolio that proves you can do the job.
- Lessons: —
- Labs: —
- Projects: —
- Level: Beginner
Curriculum
- Setup & Dev Environment — Docker, Git, uv, CUDA, IDE setup, linear algebra, calculus, autodiff, probability, vector search
- Python Fundamentals — Variables & control flow, functions & modules, NumPy & pandas, testing & Pydantic, FastAPI & async
- SQL & Data Engineering — SQL foundations, joins & data modeling, window functions, feature engineering & pipelines
- ML Fundamentals — Supervised vs unsupervised, linear regression, gradient descent, classification, trees & boosting, clustering, reinforcement learning & RLHF, metrics & validation, error analysis
- Deep Learning — Neural networks from scratch, backpropagation, PyTorch, CNNs, RNNs, training dynamics & regularization, distributed training (DDP, FSDP)
- Transformers — Attention intuition, attention from scratch, tokenization & embeddings, transformer architecture, pretraining & fine-tuning, hands-on LoRA fine-tuning
- LLM Engineering — Model landscape, prompt engineering, structured outputs, context window optimization, multimodal AI, local LLMs & quantization, cost-aware model routing, DSPy prompt optimization
- RAG Systems — RAG decision framework, document chunking & ingestion, embeddings & vector stores, hybrid retrieval & reranking, query rewriting, citations & abstention UX, RAG evaluation, production deployment, RAG access control & permissions
- AI Agents — Agent loop & tool use, state & memory workflows, human approval & permissions, agent evals & observability
- Evaluation & Safety — Eval fundamentals, red teaming & safety, LLM system evals, RAGAS framework, production observability, responsible AI & fairness, model cards & documentation
- Deployment & LLMOps — Docker & CI/CD, model serving, optimization & quantization, vLLM serving, MLOps monitoring, shadow deployments & safe rollout
- Cloud Deployment — Cloud foundations (AWS/GCP/Azure), deploying models on AWS, data storage & cost, multicloud ops
- Career, Portfolio & Capstone Demo Day — Portfolio building, resume & LinkedIn, system design interviews, STAR stories, interview prep, job search OS, capstone demo, defending AI-assisted work
- DSA & Coding Interviews — Arrays & strings, trees & graphs, dynamic programming, ML coding patterns
Skills You Will Learn
- Python
- PyTorch
- RAG
- Agents
- Evals
- MLOps
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