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Unit 3 | Podcast 01 – Features and the Curse of Dimensionality
Machine Learning models do not learn from raw data directly — they learn from features.
This episode introduces the idea of features, explains why too many features can harm learning, and explores the curse of dimensionality that motivates feature engineering.
Key topics:
Features: What models actually learn from data.
High-dimensional data: When more information becomes a problem.
Curse of dimensionality: Why distance, sparsity, and performance degrade.
Motivation: Why Unit 3 is essential in the ML pipeline.
This episode builds the conceptual foundation for feature selection and extraction.
Series: Mindforge ML
Produced by: Chatake Innoworks Pvt. Ltd.
Initiative: MindforgeAIhttps://internship.chatakeinnoworks.com
View all episodes
By CI CodesmithMachine Learning models do not learn from raw data directly — they learn from features.
This episode introduces the idea of features, explains why too many features can harm learning, and explores the curse of dimensionality that motivates feature engineering.
Key topics:
Features: What models actually learn from data.
High-dimensional data: When more information becomes a problem.
Curse of dimensionality: Why distance, sparsity, and performance degrade.
Motivation: Why Unit 3 is essential in the ML pipeline.
This episode builds the conceptual foundation for feature selection and extraction.
Series: Mindforge ML
Produced by: Chatake Innoworks Pvt. Ltd.
Initiative: MindforgeAIhttps://internship.chatakeinnoworks.com