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Unit 3 | Podcast 03 – Feature Extraction, PCA and Practical Challenges
Sometimes selecting features is not enough — new features must be created.
This episode explores feature extraction and dimensionality reduction, focusing on techniques like PCA and LDA, along with their practical limitations.
Key topics:
Feature extraction: Creating new representations from data.
Dimensionality reduction: Learning in lower-dimensional spaces.
PCA: Variance-based feature transformation.
LDA: Supervised dimensionality reduction.
Challenges: Interpretability, data leakage, and overuse.
This episode completes Unit 3 by linking feature engineering to model performance.
Series: Mindforge ML
Produced by: Chatake Innoworks Pvt. Ltd.
Initiative: MindforgeAIhttps://internship.chatakeinnoworks.com
View all episodes
By CI CodesmithSometimes selecting features is not enough — new features must be created.
This episode explores feature extraction and dimensionality reduction, focusing on techniques like PCA and LDA, along with their practical limitations.
Key topics:
Feature extraction: Creating new representations from data.
Dimensionality reduction: Learning in lower-dimensional spaces.
PCA: Variance-based feature transformation.
LDA: Supervised dimensionality reduction.
Challenges: Interpretability, data leakage, and overuse.
This episode completes Unit 3 by linking feature engineering to model performance.
Series: Mindforge ML
Produced by: Chatake Innoworks Pvt. Ltd.
Initiative: MindforgeAIhttps://internship.chatakeinnoworks.com