Over the last few months, I’ve been working on a small project to organize my knowledge of machine learning algorithms, data processing techniques, and evaluation metrics.
At first, it was just a personal learning exercise, meant to better understand the concepts and keep everything structured. But then I realized it could also be useful for others who are trying to navigate the ML landscape, so I decided to make it public.
What’s the goal?
The purpose of ML Compass Guide is to make the world of machine learning easier to navigate.
A clear decision map that shows where each algorithm belongs.
Explanations of algorithms.
Practical code examples.
A collection of metrics methods that help you evaluate models.
Current state
The project is still in its early stages (so expect a few rough edges 😅). Some sections are complete, others are just placeholders for now. But I’ll keep expanding it step by step.
How you can help
I’d love to get feedback from the community!
Is the structure clear?
What would make it more useful for you?
Are there algorithms/metrics you’d like to see next?
👉 You can check it out here: mlcompassguide.dev
Thanks for reading.
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