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Feature engineering is an important component in (tabular) machine learning problems, which can be easily integrated into an existing model. The tabular data structure limits the models' capabilities to learn the relationships between features, and adding handcrafted features can significantly boost their performance. We'll teach best practices for feature engineering techniques specific to tabular data building off our teams' collective experience competing in data science competitions such as Kaggle and RecSys. We're going to use RAPIDS, an open-source software that accelerates the whole data science pipeline from data preprocess/engineering to machine learning on GPU. Become familiar with RAPIDS' pandas/scikit-learn-like Python API and experience the blazing speed-up it provides.
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