zuzoovn/machine-learning-for-software-engineers

This repository is one person's multi-month study plan for moving from working software engineer to machine learning engineer. Machine learning is the branch of software where programs learn patterns from data instead of being told explicit rules, and a machine learning engineer is someone who turns those models into working systems. The author, Nam Vu, is a self-taught mobile developer with a software engineering degree (not computer science) and only a small amount of university maths. He wrote the plan as he taught himself, and shared the whole thing as a public roadmap that other people in the same position can follow. The project name describes the angle: top-down learning. Rather than starting with calculus, linear algebra, and statistics and working up to applications, the plan deliberately starts with hands-on work and abstracts most of the maths away from the beginner. The author argues that this practice-first, theory-later loop suits programmers, and contrasts practical machine learning (cleaning data, gluing libraries together, writing the code that squeezes answers out of messy real data) with theoretical machine learning (the cleaner world of maths and idealised models). The README is the plan itself, organised as a long table of contents that the reader is meant to work through in order. Sections include prerequisite knowledge, a daily plan, motivation, several different machine-learning overview readings, machine learning mastery resources, machine learning algorithms, beginner books, practical books, Kaggle knowledge competitions (Kaggle is a site that hosts data-science contests), video series, MOOCs (massive open online courses), general resources, advice on becoming an open-source contributor, games, podcasts, communities, conferences, interview questions, and a list of companies the author admires. It is not a software project itself: there is no code to run, only Markdown content pointing to books, articles, courses, videos, and competitions. The reader is expected to fork the repository and tick items off using GitHub's task-list markdown as they go. The plan was inspired by jwasham's Coding Interview University, and has been translated into Brazilian Portuguese, Simplified and Traditional Chinese, and French. The author notes that the plan will take years for someone starting from scratch, less if you already know parts of the material.