Originally published at: https://www.datacamp.com/community/blog/scikit-learn-cheat-sheet
Most of you who are learning data science with Python will have definitely heard already about scikit-learn, the open source Python library that implements a wide variety of machine learning, preprocessing, cross-validation and visualization algorithms with the help of a unified interface.
If you’re still quite new to the field, you should be aware that machine learning, and thus also this Python library, belong to the must-knows for every aspiring data scientist.
With thisscikit-learn cheat sheet, you’ll go through the basic steps to implement machine learning algorithms successfully: you'll see how to load in your data, how to preprocess it, how to create your own model to which you can fit your data and predict target labels, how to validate your model and how to tune it further to improve its performance.
Go here to see the cheat sheet.
In short, this cheat sheet will kickstart your data science projects: with the help of code examples, you’ll have created, validated and tuned your machine learning models in no time.
So what are you waiting for? Time to get started!
Begin with our scikit-learn tutorial for beginners, which will introduce you to the steps that you will need to undertake to do machine learning: data exploration, data preprocessing, the construction of your machine learning model, model validation and tuning the model. In this all, you’ll make use of Python’s data visualization library matplotlib to visualize your results.
PS. Don’t miss our Bokeh cheat sheet, the Pandas cheat sheet or the Python cheat sheet for data science.
Originally published at www.datacamp.com.