Scikit-Learn Cheat Sheet: Python Machine Learning

Scikit-learn cheat sheet: #machinelearning with #Python -

  • Most of you who are learning data science with Python will have definitely heard already about , 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.
  • This  cheat sheet will introduce you to the basic steps that you need to go through 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.
  • 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.
  • In addition, you’ll make use of Python’s data visualization library  to visualize your results.

A handy scikit-learn cheat sheet to machine learning with Python, including code examples.

@DataCamp: Scikit-learn cheat sheet: #machinelearning with #Python –

, 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. 

works, this machine learning cheat sheet might come in handy to get a quick first idea of the basics that you need to know to get started. 

Either way, we’re sure that you’re going to find it useful when you’re tackling machine learning problems!  

cheat sheet will introduce you to the basic steps that you need to go through 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. 

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! 

PS. Don’t miss our Bokeh cheat sheet, the Pandas cheat sheet or the Python cheat sheet for data science. 

Scikit-Learn Cheat Sheet: Python Machine Learning