The 51k-star study log for learning ML in public

What it does This repository is a daily learning journal documenting the author’s 100-day sprint through machine learning fundamentals. Each entry mixes conceptual notes, links to external coursework, hand-drawn infographics, and small Python implementations using scikit-learn, TensorFlow, and Keras. The curriculum runs the standard undergraduate ML syllabus: data preprocessing, linear and logistic regression, SVMs, tree-based methods, clustering, and introductory deep learning.

The interesting bit The project treats GitHub as a public study planner rather than a code library. The author mixes original infographics with homework borrowed from Coursera, Caltech’s CS 156, Bloomberg’s ML course, and 3Blue1Brown math playlists. At 51k stars, it appears many people use someone else’s discipline as their own curriculum.

Key highlights

• Daily log format covering preprocessing through CNNs, with mid-sprint detours to refresh Linear Algebra and Calculus via 3Blue1Brown
• Original infographics for concepts like Logistic Regression, SVMs, Decision Trees, Random Forests, and K-Means Clustering
• Code implementations in Python, mostly using scikit-learn (SVM with kernel trick, K-NN, Decision Trees, etc.)
• Heavy integration of external resources: Siraj Raval’s challenge, Andrew Ng’s Deep Learning Specialization, and the Python Data Science Handbook
• Datasets collected in-repo for the exercises

Caveats

• Quality is intentionally uneven: some days are just video notes or article links, and at least one code link is broken (Day 44)
• The author admits documentation help was needed early on, so Markdown formatting and explanations vary
• This is a personal syllabus, not a library; expect scattered notebooks rather than a cohesive framework

Verdict Use it as a structured checklist and morale boost while revisiting ML basics. Skip it if you need production tools, original research, or a maintained framework.