From mobile dev to ML engineer: a nightly, self-taught syllabus

What it does

This repository is a curated, day-by-day curriculum compiled by a self-taught mobile developer aiming to transition into a machine learning engineering role. It collects articles, videos, books, MOOCs, Kaggle competitions, and interview questions into a single checklist-style outline meant to be tackled sequentially. The author estimates the full path takes years, assuming roughly four hours of study per night.

The interesting bit

Instead of drowning in multivariate calculus and linear algebra upfront, the plan takes a top-down, “practice — learning — practice” approach that abstracts most of the math for beginners. It is explicitly inspired by the famous “Coding Interview University” repo, but swaps algorithmic puzzles for datasets and real-world mess.

Key highlights

• Top-down, results-first structure designed for engineers who already know how to ship code but lack formal ML training.
• Heavy emphasis on practical application first, theory second, to avoid the paralysis common in math-heavy courses.
• Includes a broad mix of resources: blog posts, video lectures, beginner and practical books, podcasts, games, and community links.
• Uses GitHub-flavored task lists, so the outline doubles as a personal progress tracker.
• Author is transparent about the timeline: it is a long, multi-year commitment, not a weekend crash course.

Caveats

• Some linked video resources require enrolling in specific Coursera or EdX sessions that may not be currently active, leaving gaps until public replacements are added.
• The README warns that a software engineering background alone is not enough; you will still need to pick up probability, statistics, and distributed computing along the way.

Verdict

Grab this if you are a working developer with stamina and a high tolerance for self-directed, multi-year sprints. Skip it if you are looking for a contained, instructor-led course or a quick credential.