Feature Engineering for Machine Learning
Learn the most popular Feature Engineering techniques for Data Science.
Description
Dive into the most popular methods of Feature Engineering! Create additional features for a model that determines whether or not somebody will sign up for our product. We’ll look at four popular types of feature engineering - constructing features using data living in our SQL database, manipulating our data in pandas dataframes, using third party data vendors and ingesting data from public APIs. We'll work through the code for each of these techniques and build out the corresponding features. Lastly, we'll check out our new features' correlations to our target variable - what we are trying to predict.
These techniques can be applied to a variety of models and feature stores. They will bolster model performance, as more informative data increases model performance. You'll also enhance your company's data assets. You will be able to apply the concepts learned here to many models throughout your organization!
This course is best for those with beginner to senior level Python and Data Science understanding. For more beginner levels, feel free to dive in and ask questions along the way. For more advanced levels, this can be a good refresher on Feature Engineering, especially if you haven't worked with the techniques described. Hopefully you all enjoy this course and have fun with this project!
What You Will Learn!
- Deep dive into popular Feature Engineering techniques for Supervised Machine Learning
- Familiarize yourself with third party data vendors
- Construct new features from within your SQL database/pandas dataframe
- Ingest and manipulate valuable data from public APIs into features
Who Should Attend!
- Beginner to Senior level Data Scientists, Machine Learning Engineers, Data Analysts, and other tech professionals.