notes

Why we included this project

Rather than a video course or a pile of code snippets, this project is a set of plain-text notes that walk through the major areas of machine learning in order. The author covers Bayesian inference, causal reasoning, knowledge representation, NLP, and reinforcement learning in concise markdown files that are self-contained enough to read on their own. That makes the repository handy as a structured refresher before an interview, a loose syllabus for a study group, or a quick reference when a paper uses a term you have not seen in a while. It stays focused on concepts rather than implementation, so you come away with the mental models needed to approach original papers and libraries. Expect a learning companion, not a deployable tool.