feature-selection-for-machine-learning and feature-engineering-for-machine-learning
These are complementary sequential steps in a machine learning pipeline: feature engineering creates and transforms raw variables into meaningful inputs, while feature selection identifies which of those engineered features to retain for model training.
About feature-selection-for-machine-learning
solegalli/feature-selection-for-machine-learning
Code repository for the online course Feature Selection for Machine Learning
This project helps data scientists and machine learning engineers refine their datasets by identifying and removing irrelevant or redundant features. It takes raw datasets with many variables and outputs a more focused dataset, ready for building more efficient and accurate predictive models. The end-user is a data practitioner looking to improve model performance and interpretability.
About feature-engineering-for-machine-learning
solegalli/feature-engineering-for-machine-learning
Code repository for the online course Feature Engineering for Machine Learning
This project helps data scientists and machine learning practitioners prepare raw datasets for building predictive models. It provides code examples to clean, transform, and create new variables from various data types like numbers, text, and dates. You'll input messy or incomplete datasets and learn techniques to output clean, well-structured features ready for machine learning algorithms.
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