Feature Engineering Case Study in Python | Data Science Online Course by Udemy

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A Complete Introduction to Feature Engineering

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Feature Engineering Case Study in Python

Course OverviewThe quality of the predictions coming out of your machine learning model is a direct reflection of the data you feed it during training. Feature engineering helps you extract every last bit of value out of data. This course provides the tools to take a data set, tease out the signal, and throw out the noise in order to optimize your models. The concepts generalize to nearly any kind of machine learning algorithm. In the course you’ll explore continuous and categorical features and shows how to clean, normalize, and alter them. Learn how to address missing values, remove outliers, transform data, create indicators, and convert features. In the final sections, you’ll to prepare features for modeling and provides four variations for comparison, so you can evaluate the impact of cleaning, transforming, and creating features through the lens of model performance. What You’ll LearnWhat is feature engineering?Exploring the dataPlotting featuresCleaning existing featuresCreating new featuresStandardizing featuresComparing the impacts on model performanceThis course is a hands on-guide. It is a playbook and a workbookintended for you to learn by doing and then apply your new understanding to the feature engineering in Python. To get the most out of the course, I would recommend working through all the examples in each tutorial. If you watch this course like a movie you’ll get little out of it. In the applied space machine learning is programming and programming is a hands on-sport. Thank you for your interest in Feature Engineering Case Study in Python. Let’s get started!

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