AI Expert Roadmap
A set of study-path charts for becoming a data scientist, ML or AI expert.
- Stars
- 31.3k
- Forks
- 2.6k
- Licence
- MIT
- Language
- JavaScript
- Last update
- 1.1 years ago
- Type
- Learning resource
No commits in over a year — check for forks or alternatives before adopting.
What it is
This repository holds a set of visual roadmaps, mostly image charts, that lay out topics and technologies to learn for careers in data science, machine learning, deep learning, and data engineering. The authors, AMAI GmbH, made them for new employees and shared them publicly. An interactive version with links for each item is hosted on their website.
- Compare the Data Science, Machine Learning, and Deep Learning roadmap charts to see how the topics differ
- Use the Fundamentals chart as a checklist of starting topics
- Follow the linked interactive version to explore each bullet point
- Check the Data Engineer and Big Data Engineer charts if you are considering that path
Great for
- Beginners who are unsure what to learn next in AI or data science
- Students planning a study path across several AI-related fields
- People who want an overview of the landscape before picking specific tools
Not ideal for
- Readers looking for hands-on code, tutorials, or runnable projects, since the README describes only charts and links
- Anyone needing current guidance, since the title refers to 2022 and the README does not say how often it is updated
How to get it
- 1
Read it online
Clone the repository
git clone https://github.com/AMAI-GmbH/AI-Expert-Roadmap - 2
Run it
- Open the repository on GitHub and view the roadmap images in the README
- Pick the roadmap that matches your goal, such as Data Science or Machine Learning
- Open the interactive version at i.am.ai/roadmap for links on each topic
- Work through the listed topics using outside courses or books
- 3
Get the most out of it
- Use the interactive roadmap at i.am.ai/roadmap to get links for each bullet point
- Star or watch the repository to get notified of new content, as the README suggests
- Read the disclaimer: choose tools for their fit to the job, not because they are trendy
- Check the contributing docs if you want to suggest updates
Downloads
Strengths
- Gives a broad map of many AI-related fields in one place
- The README states its purpose is orientation, not promoting trendy tools
- Free and MIT-licensed, with an active contribution process described
Trade-offs
- The charts are images and the README gives no written detail for each topic
- The title says 2022 and the content may be dated
How it compares
| Project | Stars | Type | Level | Updated | Best for |
|---|---|---|---|---|---|
| AI-Expert-Roadmap (this) | 31.3k | Learning resource | beginner | 1.1 years ago | Beginners who are unsure what to learn next in AI or data science |
| mit-deep-learning | 10.5k | Learning resource | beginner | 2.8 years ago | Students following MIT deep learning courses |
| machine-learning-for-trading | 21.7k | Learning resource | intermediate | 8 days ago | Readers of the Machine Learning for Trading book who want to run its code |
| awesome-artificial-intelligence | 16.8k | Curated list | beginner | 3 months ago | Software developers who want to learn the foundations behind language models and agents |
| spaCy | 34k | Library | intermediate | 10 days ago | Python developers building text-processing features into an application |
| AI-For-Beginners | 70.5k | Learning resource | beginner | 13 days ago | Beginners who want a structured, self-paced introduction to AI |
Stats synced from GitHub. The assessment above was drafted with AI from the project's README on 2026-10-29 and may miss recent changes — check the README before relying on a command. MIT licence: free to use, modify and distribute, including commercially, as long as you keep the copyright notice.