Python Machine Learning: Scikit-Learn Tutorial

Machine learning is a branch in computer science that studies the design of algorithms that can learn. Typical tasks are concept learning, function learning or "predictive modeling", clustering and finding predictive patterns. These tasks are learned through available data that were observed through experiences or instructions, for example. The hope that comes with this discipline is that including the experience into its tasks will eventually improve the learning. But this improvement needs to happen in such a way that the learning itself becomes automatic so that humans like ourselves don't need to interfere anymore is the ultimate goal. Today's scikit-learn tutorial will introduce you to the basics of Python machine learning: If you're more interested in an R tutorial, take a look at our Machine Learning with R for Beginners tutorial. The first step to about anything in data science is loading in your data. This is also the starting point of this scikit-learn tutorial. This discipline typically works with observed data. This data might be collected by yourself or you can browse through other sources to find data sets. But if you're not a researcher or otherwise involved in experiments, you'll probably do the latter.