Repository: mrdbourke/machine-learning-roadmap
Branch: master
Commit: b5006b916177
Files: 2
Total size: 2.4 KB

Directory structure:
gitextract_si2m0rt2/

├── LICENSE
└── README.md

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FILE: LICENSE
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MIT License

Copyright (c) 2020 Daniel Bourke

Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:

The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.

THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE.


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FILE: README.md
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# 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.