Do you need a little help learning Scikit-Learn in Python? Or maybe you just finding it hard to remember all the different commands to perform different operations? All of those formulas can be confusing and hard to remember. Have no fear!! I have put together 10 of the Best Python Scikit-Learn cheat sheets for you to print and hand next to all your other cheat sheets on the wall above you desk. Take a little time each day to review your cheat sheets and you will have it down in no time!
This Scikit-Learn cheat sheet from DataCamp will kick start your data science project by introducing you to the basic concepts of machine learning algorithms successfully. This cheat sheet is for those who have already started to learn Python packages and for those who would like to take a quick look to get a first idea of the basics for total beginners!
Pros: This cheat sheet is rated ‘E’ for everyone!! Information is sectioned in blocks for easier reading
Cons: The bright red can be distracting to some
This Scikit-Learn cheat sheet is done in cool blues than its red cousin above. The information is broken down into blocks to making it easier to digest. This cheat sheet will show you the basics through examples so you can learn to preprocess your data for your projects.
Pros: Rated ‘E’ for everyone!! Information is easily digestible.
Cons: none that I can see.
In collaboration with IBM, Intellipaat has gone one step further with this cheat sheet by providing not only headers in the blocks so you know what you are doing but also in what part of the process you are at! Pre- and Post-processing your data model, with all the steps for you in one handy reference.
Pros: Rated ‘E’ for everyone. It has blocks with steps inside so you don’t forget what commands are used in Pre/PostProcessing, Working the model and evaluating the performance.
Cons: none that I can see.
This cheat sheet is great for those who are only needing a quick reference for the definitions of scikit-learn expressions. The sheet is pretty spartan compared to the others in examples but also goes into more depth than the others on definitions. I would not suggest this particular cheat sheet to a total beginner in data science or in Scikit-Learn. I would rate this sheet at ‘I’ for the Intermediate learner.
Pros: Great on definitions on multiple expression types in Scikit-Learn.
Cons: Too spartan for beginners, green background can be distracting.
This sheet is also intended for the Intermediate learner of Scikit-Learn. Showing examples for Linear Regressions, Naïve Bayes, k-nearest neighbors, K means, validating the model and Training and test sets, you would best already knowing what the definition of the above expressions are and what they can do. This handy reference is nice to have near if you just need to remember how to write your expression.
Pros: Handy for the Intermediate learner, comes with code examples
Cons: Not for beginners.
Here on becominghuman.ai, cheat sheets show not only definitions, but also flow charts to help you check documentation and which estimator is the right one for the job, which can be difficult to do. This cheat sheet is for the Intermediate learner
Pros: Great for Intermediate learners, in-depth definitions on expressions
This cheat sheet shows you the mapping processes of machine learning thru mapping out what each classification, clustering, regression and dimensionality reduction It is a great map to help show you how the expressions are interconnected.
Pros: Great visual
Cons: Not suggested for beginners
These pdfs are a combination of 3 actually, but each one goes into depth of Classification, Clustering and Regression. This set of 3 are perfect for a complete beginner as it gives you not only definition and code, but also tips, when to use it and how it works!! Enthought made sure to cover everything for you, so don’t worry if you forget or need a refresher on how it all works!
Pros: Rated ‘E’ for everyone!! Goes in depth for the total beginner
Cons: Can be a lengthy read
This cheat sheet is put together beautifully showing you a step by step process on how to use scikit-learn to build and tune a supervised data model on your own!! One con is that it does not show any examples on how the expressions are used.
Pros: Nicely put together for easy readability.
Cons: For the Intermediate learner.
This last sheet is generously provided by an Instagram Data Engineer!! Lauren Glass has put together a comprehensive cheat sheet for scikit learn and has made it easy for beginners to understand!! She goes in depth on all the sections and provides definitions for each.
Pros: Easy to read and understand
Cons: None I can see
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Thanks for joining me once again!! I hope you find these cheat sheets on Scikit-Learn useful and tape them to your wall above your desk to keep them handy!! I will keep you updated on the best cheat sheets for Python and related subjects!!