Unit 1 | Podcast 06 – When Machine Learning Fails: Data, Bias, and Hidden Challenges
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Unit 1 | Podcast 06 – When Machine Learning Fails: Data, Bias, and Hidden Challenges
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About this listen
Welcome to Podcast 06 of Mindforge ML | Foundations to Intelligence,an educational podcast by Chatake Innoworks Pvt. Ltd.,published under the MindforgeAI initiative.
In this episode, we take a critical look at Machine Learning and explore animportant truth: powerful models can still fail.Understanding these limitations is essential for building responsible andreliable ML systems.
Through simple analogies and real-world scenarios, we discuss some of the mostcommon challenges faced in machine learning:
- Why data quality matters more than complex algorithms
- The meaning of “garbage in, garbage out”
- Overfitting and underfitting, and how models can mislearn
- How bias in data leads to unfair or misleading outcomes
- Ethical and practical concerns in real-world ML deployment
This episode emphasizes that machine learning is not just a technical problem,but also a human responsibility involving careful data collection, evaluation,and judgment.
Series: Mindforge ML | Foundations to Intelligence
Unit: Unit 1 – Introduction to Machine Learning
Episode: Podcast 06
Produced by: Chatake Innoworks Pvt. Ltd.
Published under: MindforgeAI
Creator: CI Codesmith