imteekay/machine-learning-research

What it solves

Machine learning research requires a broad foundation: mathematics, programming, AI theory, and domain knowledge. This project solves the problem of scattered resources and unclear learning paths by collecting a large, organized set of links to courses, books, papers, videos, and tutorials. It lets you start from basics (like linear algebra and Python) and progress to advanced topics (like transformers and graph neural networks) without needing to hunt for materials yourself.

How it works

The repository is structured as a giant table of contents. It groups hundreds of resources into logical categories: Mathematics (general math, linear algebra, statistics, calculus, optimization), Programming (algorithms, Python, ML engineering, distributed systems), Artificial Intelligence (data science, machine learning, deep learning, generative AI, reinforcement learning, causal inference, PyTorch, and specialized areas like healthcare and biology), Science (fundamentals, biology, cancer, genetics), Careers (interview prep, jobs), Research, Projects, and Community. Each category contains direct links to external lectures, papers, books, and interactive courses. The README acts as a single entry point, with a learning roadmap section providing suggested starting points for various paths.

Who it's for

This is for self‑learners, students, and researchers who want a structured path into machine learning research. It suits beginners looking for a logical progression from high‑school math to cutting‑edge AI, as well as more advanced practitioners who want targeted materials on specialized topics like geometric deep learning, multimodal ML, or ML for healthcare. The inclusion of career and community sections also makes it useful for people preparing for ML roles or seeking research connections.

Highlights

• Broad coverage: from foundations (math, Python) to frontiers (LLMs, graph neural networks, multimodal AI).
• Curated quality: includes recognized university courses (MIT, Stanford, CMU), classic textbooks, and influential papers.
• Practical extras: learning roadmaps, interview prep, coding challenges, and links to ML‑focused podcasts and Q&A.
• Domain extensions: dedicated sections for ML in healthcare, biology, and precision health.
• Community focus: lists of researchers, labs, companies, and central resources.