Machine LearningTrain, evaluate and deploy models on real data.

ML Engineer Bootcamp

Train, evaluate and serve models that pass contract tests, not notebooks that run once.

Python and some statistics; the first block reviews the maths you need.

1. Week 1Baseline ModelA scikit-learn baseline with cross-validation on a tabular dataset, in a Jupyter notebook
2. Weeks 1–2The ML toolkitA tabular model with a leak-free validation scheme, deployed as an API
3. Week 3Training LoopA PyTorch training loop with checkpoints and a validation curve on a small dataset

Computer Vision Bootcamp

From pixels to a deployed vision pipeline you can explain line by line.

Python and the ML toolkit (or the ML Engineer Bootcamp first).

1. Week 1Image AugmenterA Python script that applies flips, crops and colour shifts to a small image set
2. Weeks 1–2Images as dataA labelled dataset and a classical baseline, published
3. Week 3Transfer Learning RunA pretrained network fine-tuned on a small image set in a Jupyter notebook

Fine-Tuning and Open-Weights Bootcamp

Post-train, evaluate and deploy your own model for a real task.

Python, some PyTorch, and a GPU budget (we say how much).

1. Week 1Model LoaderA Jupyter notebook that loads an open model from Hugging Face and runs it on sample prompts
2. Weeks 1–2Open weightsA baseline eval of three open models on your task, published
3. Week 3Dataset CardA data card for a small dataset with sources, licences and a cleaning script

Physical AI and Robotics Bootcamp

Perception, control and simulation for machines that move.

Python and some linear algebra; the ML toolkit helps.

1. Week 1Simulator SetupA simulated robot arm that moves to a target position from a Python script
2. Weeks 1–2Simulation firstA simulated robot completing a task, with the code published
3. Week 3Sensor ReplayA script that replays recorded camera and lidar data and plots each frame