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AI and machine learning are no longer the technology of the future: they’re forever ingrained in every industry. But algorithms are only as good as their data, making Feature Engineering essential. We’ve launched a new Feature Engineering course that will provide an opportunity to work hands-on through a complete feature development process, including:
- Discover which features in a dataset are likely to be the most useful
- Learn to create the the right kind of features for your particular model
- Assess whether new features help or harm performance
In the new course, you’ll learn a variety of feature engineering techniques to improve your own machine learning projects
Lessons
1) What Is Feature Engineering
Learn the steps and principles of creating better features
2) Mutual Information
Locate features with the most potential.
3) Creating Features
Transform features with Pandas to suit your model.
4) Clustering With K-Means
Untangle complex spatial relationships with cluster labels.
5) Principal Component Analysis
Discover new features by analyzing variation.
6) Target Encoding
Boost any categorical feature with this powerful technique.
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