Francis Burnet – AI Engineering Portfolio

A guided portfolio of capstones, live demos, and production-minded AI engineering work across data science, machine learning, and deep learning.

Francis Burnet headshot
Futuristic AI learning journey wall showing progression from data science to deep learning

Capstones

Incremental Capstone Journey

A step-by-step collection of course capstones, each organized with problem statements, notebooks, outputs, artifacts, and reviewer-friendly explanations.

Incremental Capstone Hub

Each session page follows the same structure: objective, requirement checklist, code walkthrough, parameter controls, and generated outputs, with source folders staged inside the production tree for FrancisBurnet.

12 Sessions — One Continuous Build

Every capstone builds on the last. Sessions 1–4 cover applied data science, sessions 5–9 cover machine learning, and sessions 10–12 cover deep learning — each with a live results feed and linked Jupyter notebook.

Select a session below to view its objective, dataset, and live model outputs.

Applied Data Science

Capstones 1 through 4 from the applied data science track.

Capstone 1 Requirement-first walkthrough for data inspection, cleaning guidance, JSON export, and the first NSMES handoff dataset.
Capstone 2 Focused walkthrough for feature scaling, memory comparison, statistics, and the updated NSMES healthcare dataset.
Capstone 3 Detailed walkthrough of pivot analysis, grouped reporting, and distribution tables built from the NSMES handoff data.
Capstone 4 Chart-driven healthcare analysis page covering plotting choices, saved figures, and observation reporting.

Machine Learning

Capstones 5 through 8 from the machine learning track.

Capstone 5 Machine learning project page for bike rental modeling, notebook evidence, and exported artifacts.
Capstone 6 Machine learning project page for income prediction, supporting visuals, and staged model artifacts.
Capstone 7 Machine learning capstone page covering unsupervised analysis, visuals, and saved outputs.
Capstone 8 Machine learning capstone page for recommendation or ratings-based analysis with walkthrough and artifacts.

Deep Learning

Capstones 9 through 12 from the deep learning track.

Capstone 9 Deep learning capstone page for bank churn modeling, staged outputs, and concept-driven neural network explanations.
Capstone 10 Deep learning capstone page for face mask detection, model comparison, screenshots, outputs, and demo-ready artifacts.
Capstone 11 Deep learning capstone page for text analysis, review classification, and supporting model walkthrough content.
Capstone 12 Deep learning capstone page for denoising autoencoder work, reconstruction outputs, and supporting explanations.