Capstone Projects

Portfolio-ready AI projects from classical ML through GenAI product launch.

0 of 10 completed

No Completed Projects Yet

Complete ML capstones as you progress and they'll appear here.

Upcoming Projects

Phase 1ML Foundations: From Data Analyst to First Real Models

Capstone: Linear Models, End to End

On one real tabular dataset, deliver a data audit, a from-scratch linear model, a sklearn parity model, regularization and calibration sweeps, cross-validated metrics with uncertainty, and a one-page model card that an interviewer or manager could read in two minutes.

Phase 2Classical Supervised ML in Depth

Capstone: Classical ML Case Study

Build a supervised tabular ML case study end to end: data audit, baseline, ensemble, tuned booster, calibration, cross-validated metrics with intervals, SHAP-based explanation, model card, and a packaged FastAPI demo.

Phase 3Unsupervised Learning, Recommenders, and Retrieval Foundations

Capstone: Discovery, Retrieval, or Recommender System

Build a segmentation, anomaly, recommender, topic, or retrieval system and prove usefulness with examples, stability checks, and a human review workflow.

Phase 4Neural Networks From Scratch to PyTorch

Capstone: Neural Network From Scratch to PyTorch

Build a tiny neural net from scratch, prove gradients, then rebuild it in PyTorch and train a real classifier with debugging notes and error analysis.

Phase 5Deep Learning Architectures, Vision, and Multimodal Perception

Capstone: Multimodal Perception System

Build a computer vision or multimodal perception system with dataset auditing, model comparison, qualitative error review, deployment, and safety notes.

Phase 6NLP Foundations and Classic Neural NLP

Capstone: Classic NLP System

Build a text classification, NER, QA, summarization, or retrieval baseline system using classic NLP and early neural methods before transformers.

Phase 7Transformers and LLM Internals

Capstone: Tiny GPT and Transformer Internals

Build a tiny GPT-style model, implement or inspect the tokenizer, train or fine-tune it on a small corpus, compare with Hugging Face, and write an evaluation report.

Phase 8Reinforcement Learning, LLM Post-Training, and Evaluation

Capstone: Fine-Tuned and Evaluated LLM

Fine-tune or adapt an open model with LoRA or an SFT-style workflow, then evaluate usefulness, safety, factuality, cost, and release risk.

Phase 9RAG, Agents, and Production LLM Applications

Capstone: Production RAG or Agent App

Build a serious RAG or agent product with evals, citations or tool traces, security tests, cost controls, observability notes, and a deployed demo.

Phase 10Generative Images, Video, 3D, Research, and Production

Capstone: 2026 AI Product Launch

Launch a portfolio-grade AI product that can include ML, DL, LLMs, RAG, agents, diffusion, or multimodal features, with evaluation, deployment, monitoring, governance, and a case-study writeup.