Six structured courses from Python foundations to LLM engineering — built by researchers who deploy models in production, not just teach from textbooks. Weekend live sessions, self-paced access, lifetime materials.
Weekend live sessions with the instructor and a cohort of peers. Assignments reviewed weekly. Project work at the end of each module. Start dates announced per cohort.
All recorded sessions, notebooks, and materials with lifetime access. Work through at your own pace, submit projects for review, and receive a certificate on completion.
Take them in order or jump to your level. Each course ends with a project and a verified certificate of completion.
NumPy, Pandas, Matplotlib, and Seaborn — the data layer every AI practitioner needs. Includes hands-on EDA on real datasets.
Supervised & unsupervised learning, feature engineering, scikit-learn, and model evaluation. Build and ship your first ML pipeline.
Neural network fundamentals, CNNs, RNNs, and training loops with PyTorch. Covers backprop, regularisation, and transfer learning.
Attention mechanisms, BERT, GPT, fine-tuning, and HuggingFace pipelines. Build a text classification and summarisation service.
Object detection with YOLO, semantic segmentation, real-time video analysis, and model deployment. Project: a live detection API.
RAG pipelines, LoRA/QLoRA fine-tuning, LangChain agents, evaluation frameworks, and domain LLM deployment at production scale.
Pricing on request. Contact us for fees, schedules & group discounts →
CS, ECE, or related background. Beginner courses require only basic Python. Intermediate courses require ML Foundations or equivalent.
Engineers and analysts upskilling into AI. Weekend schedule keeps learning compatible with a full-time job.
Praxis participants can take concurrent courses to deepen the theory behind the project work they're shipping.
Reach out for the current cohort schedule, fees, and group enrolment options.