zuzoovn/machine-learning-for-software-engineers

★ 28,763Audience · developerComplexity · 1/5StaleLicenseSetup · easy

Mindmap

mindmap
  root((repo))
    What it does
      Study roadmap
      Resource checklist
      Progress tracker
    Learning path
      Beginner topics
      Intermediate ML
      Deep learning
      Advanced concepts
    Resource types
      Online courses
      Books
      Kaggle projects
      Podcasts
    Approach
      Practice first
      Theory second
      Hands-on focus
    Audience
      Software engineers
      Career switchers
      Self-taught learners

mindmap root((repo)) What it does Study roadmap Resource checklist Progress tracker Learning path Beginner topics Intermediate ML Deep learning Advanced concepts Resource types Online courses Books Kaggle projects Podcasts Approach Practice first Theory second Hands-on focus Audience Software engineers Career switchers Self-taught learners

Things people build with this

USE CASE 1

Plan a self-directed transition from software engineering into machine learning roles without a formal degree.

USE CASE 2

Build a curated learning schedule by working through the checklist of courses, books, and Kaggle competitions in order.

USE CASE 3

Prepare for machine learning interviews by studying the recommended algorithms and deep learning fundamentals.

USE CASE 4

Track your progress through machine learning fundamentals by forking the repo and checking off completed resources.

Tech stack

Machine LearningDeep LearningPythonKaggle

Getting it running

Difficulty · easy Time to first run · 5min

Use freely including commercial. Credit the author, share derivative work under the same license.

In plain English

This repository is a "top-down learning path", a self-study plan for software engineers who want to retrain as machine-learning engineers. The author, Nam Vu, wrote it for himself as a multi-month plan to go from mobile developer with no computer-science degree to a machine-learning engineer. The README says the approach is unconventional because it is "top-down and results-first": instead of grinding through the math first, the plan tries to stay mainly hands-on and abstracts away most of the math for a beginner. It draws on a "practice, learning, practice" idea, where you start with projects and problems, drop into the theory when you are ready, then return to harder practice. The README also quotes a distinction between practical machine learning (querying databases, cleaning data, gluing algorithms and libraries together) and theoretical machine learning (math, abstraction, idealized scenarios). The repository was inspired by Coding Interview University and is translated into Brazilian Portuguese, Chinese (simplified and traditional), and French. The content itself is a long ordered outline: a table of contents covering the daily plan, prerequisite knowledge, video resources, books for beginners and practitioners, Kaggle competitions, MOOCs, podcasts, communities, conferences, and interview questions. You are meant to fork the repo and use GitHub task-list checkboxes to track progress. Someone would use this if they are a working software developer who wants a curated, sequenced path into machine learning without designing the curriculum themselves. The full README is longer than what was provided.

Copy-paste prompts

Prompt 1

I'm a software engineer with no ML background. Walk me through the first 3 months of this study plan, what should I focus on first?

Prompt 2

Using this roadmap, create a 6-month learning schedule for me that balances theory, online courses, and hands-on Kaggle projects.

Prompt 3

Which resources from this checklist are best for preparing for machine learning engineer interviews?

Prompt 4

I've completed the beginner section. What deep learning topics should I tackle next based on this roadmap?

Prompt 5

Help me set up a learning tracker based on this repository's checklist so I can monitor my progress weekly.

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Generated 2026-05-21 · Model: sonnet-4-6 · Verify against the repo before relying on details.