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mrdbourke/machine-learning-roadmap · source

Snapshot 0 · 2026-11-04T12:47:26Z · · MIT

GitHub description at capture

A roadmap connecting many of the most important concepts in machine learning, how to learn them and what tools to use to perform them.

README at capture

# 2020 Machine Learning Roadmap (still 90% valid for 2023) 

![2020 machine learning roadmap overview](https://raw.githubusercontent.com/mrdbourke/machine-learning-roadmap/master/2020-ml-roadmap-overview.png?token=AD7ZOCOIG7IZXHDL63W6RZK7A3B6I)

A roadmap connecting many of the most important concepts in machine learning, how to learn them and what tools to use to perform them.

Namely:

1. 🤔 **Machine Learning Problems** - what does a machine learning problem look like?
2. ♻️ **Machine Learning Process** - once you’ve found a problem, what steps might you take to solve it?
3. 🛠 **Machine Learning Tools** - what should you use to build your solution?
4. 🧮 **Machine Learning Mathematics** - what exactly is happening under the hood of all the machine learning code you're writing?
5. 📚 **Machine Learning Resources** - okay, this is cool, how can I learn all of this?

See the [full interactive version](https://dbourke.link/mlmap).

[Watch a feature-length film video walkthrough](https://youtu.be/pHiMN_gy9mk) (yes, really, it's longer than most movies).

Many of the materials in this roadmap were inspired by [Daniel Formoso](https://github.com/dformoso)'s [machine learning mindmaps](https://github.com/dformoso/machine-learning-mindmap),so if you enjoyed this one, go and check out his. He also has a mindmap specifically for [deep learning](https://github.com/dformoso/deeplearning-mindmap) too.