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Sergey Fogelson
Sergey Fogelson

VP of Analytics and Measurement Sciences, Viacom

Sergey began his career as an academic at Dartmouth College, where he researched the neural bases of visual category learning and obtained his Ph.D. in Cognitive Neuroscience. After leaving academia, Sergey got into the rapidly growing startup scene in the NYC metro area, where he has worked as a data scientist in digital advertising, cybersecurity, finance, and media. He is heavily involved in the NYC-area teaching community and has taught courses at various bootcamps, and has been a volunteer teacher in computer science through TEALSK12. When Sergey is not working or teaching, he is probably hiking. (He thru-hiked the Appalachian trail before graduate school).

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Course Description

Do you know the basics of supervised learning and want to learn to use state-of-the-art models on real-world datasets? Gradient boosting is currently one of the most popular techniques for efficient modeling of tabular datasets of all sizes. XGboost is a very fast, scalable implementation of gradient boosting that has taken data science by storm, with models using XGBoost regularly winning many online data science competitions and used at scale across different industries. In this course, you'll learn how to use this powerful library alongside pandas and scikit-learn to build and tune supervised learning models. You'll work with real-world datasets to solve classification as well as regression problems.

  1. 1

    Classification with XGBoost


    This chapter will introduce you to the fundamental idea behind XGBoost - boosted learners. Once you understand how XGBoost works, you'll apply it to solve a common classification problem found in industry, namely, predicting whether a customer will stop being a customer at some point in the future.

  2. Regression with XGBoost

    After a brief review of supervised regression, you'll apply XGBoost to the regression task of predicting house prices in Ames, Iowa. Along the way, you'll learn about the two kinds of base learners that XGboost can use as its weak learners, and review how to evaluate the quality of your regression models.

  3. Fine-tuning your XGBoost model

    This chapter will teach you how to make your XGBoost models as performant as possible. You'll learn about the variety of parameters that can be adjusted to alter the behavior of XGBoost and how to tune them efficiently so that you can supercharge the performance of your models!

  4. Using XGBoost in pipelines

    Here, you'll take your XGBoost skills to the next level by incorporating your models into two end-to-end machine learning pipelines. You'll learn how to tune the most important XGBoost hyperparameters efficiently within a pipeline, as well as be introduced to some more advanced preprocessing techniques, all the while applying everything you've learned in the first three chapters. Enjoy!