Feature Selection in Machine Learning | Filter Method & Python Implementation

Course link: https://aicourse.thinkific.com/course...
00:00 – Feature Selection Methods
02:36 – Filter Methods
07:17 – Python Implementation of Filter Method
In this video, we dive into feature selection in machine learning with a special focus on the filter method and its Python implementation using Scikit-Learn. You will learn why feature selection is a crucial step in reducing dimensionality and improving model performance, along with how different approaches such as filter, wrapper, and embedded methods differ from each other. After covering the theory, we move into a hands-on Python demo using a real dataset from the UCI Machine Learning Repository, where we implement the SelectKBest technique to rank, score, and select the most important features out of thousands. By the end of this tutorial, you will clearly understand how filter methods work, how to apply them for preprocessing in classification tasks, and how they fit into the broader scope of feature engineering and dimensionality reduction. This session is part of the course “Introduction to Feature Engineering and Dimensionality Reduction” and is designed to help beginners and intermediate learners strengthen their skills in machine learning, feature engineering, and data preprocessing.

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