When working with machine learning, you typically create a model which can predict outcomes. You create this model by training it, showing it existing data. The first step then to training a model is to setup some training data, and then some testing data to ensure the model is behaving in an expected fashion. Fortunately, this is where scikit-learn comes into play. We'll chat through the basics of the library, and about testing and training data.
Watch the Python for Beginner series here: https://aka.ms/PythonBeginnerSeries
For the Full 'Intro to Python' course on Microsoft Learn: https://aka.ms/MSLearnPython
All video content will available at: https://github.com/microsoft/c9-python-getting-started/
First series: https://github.com/microsoft/c9-python-getting-started/tree/master/python-for-beginners
Second series: https://github.com/microsoft/c9-python-getting-started/tree/master/more-python-for-beginners
Third series: https://github.com/microsoft/c9-python-getting-started/tree/master/even-more-python-for-beginners-data-tools
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