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New Python Course: Machine Learning with the Experts, School Budgets

"Machine Learning with the Experts: School Budgets" is our latest course, taught by Peter Bull. It's a case study from a DrivenData machine learning competition on school budgets.

Data science isn't just for predicting ad-clicks, it's also useful for social impact! This course is a case study from a machine learning competition on DrivenData. You'll explore a problem related to school district budgeting. By building a model to automatically classify items in a school's budget, it makes it easier and faster for schools to compare their spending with other schools. In this course, you'll begin by building a baseline model that is a simple, first-pass approach. In particular, you'll do some natural language processing to prepare the budgets for modeling. Next, you'll have the opportunity to try your own techniques and see how they compare to participants from the competition. Finally, you'll see how the winner was able to combine a number of expert techniques to build the most accurate model.


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Machine Learning with the experts: School Budgets features interactive exercises that combine high-quality video, in-browser coding, and gamification for an engaging learning experience that will make you a master machine learning in python!



What you'll learn:

In the first chapter, you'll be introduced to the problem you'll be solving in this course. How do you accurately classify line-items in a school budget based on what that money is being used for? You will explore the raw text and numeric values in the dataset, both quantitatively and visually. And you'll learn how to measure success when trying to predict class labels for each row of the dataset. Start first chapter for free.

In chapter 2, you'll build a first-pass model. You'll use numeric data only to train the model. Spoiler alert - throwing out all of the text data is bad for performance! But you'll learn how to format your predictions. Then, you'll be introduced to natural language processing (NLP) in order to start working with the large amounts of text in the data.

Here in chapter 3, you'll improve on your benchmark model using pipelines. Because the budget consists of both text and numeric data, you'll learn to how build pipelines that process multiple types of data. You'll also explore how the flexibility of the pipeline workflow makes testing different approaches efficient, even in complicated problems like this one!

In the final chapter, you will learn the tricks used by the competition winner, and implement them yourself using scikit-learn. Enjoy!


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