Subject
3 entries
Fundamentals
Bookmarks
Statistics Revisited
A beginner-friendly revisit of descriptive and inferential statistics for data scientists — covering the Central Limit Theorem, confidence intervals, z-scores, and t-distributions with accessible explanations. Good refresher on the probabilistic foundations underlying most ML evaluation.
Big-O Notation Explained by a Self-Taught Programmer
A self-taught programmer's accessible guide to Big-O notation — explaining time and space complexity from first principles without assuming a CS degree. A good on-ramp for practitioners who need to reason about algorithm performance.
A Thousand-Foot View of Machine Learning
A high-level orientation to machine learning from 2009 — the major paradigms (supervised, unsupervised, reinforcement), the core families of algorithms, and when to apply each. A useful framing piece for someone entering the field.