Originally published at: https://www.datacamp.com/community/blog/scikit-learn-cheat-sheet Most of you who are learning data science with will have definitely heard already about , the open source Python library that implements a wide variety of , preprocessing, cross-validation and visualization algorithms with the help of a unified interface. Python scikit-learn machine learning 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 this 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. scikit-learn Go to see the cheat sheet. here 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 , 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 to visualize your results. our scikit-learn tutorial for beginners matplotlib PS. Don’t miss our , the or the . Bokeh cheat sheet Pandas cheat sheet Python cheat sheet for data science Originally published at www.datacamp.com .
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