Artificial Intelligence

MLx Fundamentals 25

This program offered a comprehensive 18-hour course that included theory and practical sessions covering key areas in modern machine learning, such as Introduction to ML, Deep Learning & Representation Learning, Optimisation, and Generative AI (vision & NLP).

MLx Generative AI 25

MLx Health & Bio 25

This program offered a comprehensive 23-hour course that included overview of machine learning theory in Representation Learning, Geometrical Deep Learning, Large Language Models, and Computer Vision, with in-depth applications in health and biomedical domains, including drug discovery, genomics, electronic health records, medical imaging, and clinical NLP

MLx Representation Learning & Generative AI 25

MLx Representation Learning & Generative AI 25 is currently ongoing! We aim to have the recordings uploaded here around a couple days after the lectures are finished.

Statistical Machine Learning

Master the mathematical foundations and statistical methods underlying modern machine learning. This certification covers probability theory, Bayesian modeling, optimization, and advanced statistical inference techniques essential for rigorous ML research and development.

Representation Learning

Explore cutting-edge techniques for learning powerful data representations. This certification covers self-supervised learning, transformers, multimodal models, and generative AI, preparing you for advanced roles in modern deep learning and foundation model development.

Machine Learning Researcher

A comprehensive 12-week learning plan designed to prepare for both AWS Certified Machine Learning – Specialty (MLS-C01) and Google Cloud Professional Machine Learning Engineer certifications. This plan follows an overlap-first approach, covering shared ML concepts before addressing platform-specific differences. It includes diagnostic assessment, optional refresher tracks, hands-on labs using free-tier resources, research-oriented practices, and a 2-week exam simulation phase. Structured for part-time study alongside work with emphasis on applied practice, MLOps, and exam readiness.

Machine Learning Engineer

Template plan to prepare a team for ML Engineer responsibilities and the Google Professional Machine Learning Engineer certification. Includes a core 6–8 week sprint and an extended 12–16 week track, with hands-on labs/projects, practice exam cadence, and an optional multi-cloud mapping module (AWS/Azure). Fill in placeholders for team size, weekly time budget, and cloud access.