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Machine learning cheat sheets

The process of diving deep into machine learning contains a lot of algorithms which have to be examined even if their usage is still unclear, many are maybe intuitive, lots are however based on higher-level maths which could take days or weeks to understand with only formal textbooks for help. Following are some posters I find very useful for beginner to follow by. What those following cheat sheets NOT do is explaining the concept of who each algorithm works, therefore one should read more into the depth in order to use more complex algorithms confidently.


Traditional algorithms’ use cases for beginners.




Evaluate the fitness of a model depending on the used algorithm. One should never use confusion-matrix or ROC-AUC curve for regression.



A good cheat sheet for machine learning beginners who want to learn with scikit-learn, which is the TO-GO when starting with machine learning.


Numpy cheat sheet. The famous linear algebra library is the foundation of basically every other python machine learning framework out there.


Another popular library for handling spreadsheet data.


Did I mention scikit-learn? Because I love scikit-learn.


Last scikit-learn cheat sheet for today, promised.


Some colorful visualization of how popular neural network architecture looks like.



A quite professional presentation. But whoever has to use a cheat sheet for machine learning is not a professional probably.
Published inMachine Learning

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