In this tutorial, we’ll look into the common machine learning methods of supervised and unsupervised learning, and common algorithmic approaches in machine learning
Will show you how to create a “random forest” - perhaps the most widely applicable machine learning model - to create a solution to the “Bull Book for Bulldozers” Kaggle competition, which will get you in to the top 25% on the leaderboard. You’ll learn how to use a Jupyter Notebook to build and analyze models, how to download data, and other basic skills you need to get started with machine leraning in practice.
We discuss using validation and test sets to help us measure overfitting. Then we’ll learn how random forests work - first, by looking at the individual trees that make them up, then by learning about “bagging”, the simple trick that lets a random forest be much more accurate than any individual tree.
How to read a much larger dataset - one which may not even fit in the RAM on your machine! And we’ll also learn how to create a random forest for that dataset. We also discuss the software engineering concept of “profiling”, to learn how to speed up our code if it’s not fast enough - especially useful for these big datasets. Next, we do a deeper dive in to validation sets, and discuss what makes a good validation set, and we use that discussion to pick a validation set for this new data. In the second half of this lesson, we look at “model interpretation” - the critically important skill of using your model to better understand your data. Today’s focus for interpretation is the “feature importance plot”, which is perhaps the most useful model interpretation technique…
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