📊 MICRO-DECK 2: MACHINE LEARNING & DATA QUALITY

Question 1

Relationship between AI, ML, and Deep Learning

Answer 1

AI = broad field → ML = learning from data → Deep Learning = neural-network-based ML.

Question 2

What role does training data play in ML?

Answer 2

ML models learn patterns and adjust parameters based on training data.

Question 3

What does “Garbage In, Garbage Out” mean?

Answer 3

Poor-quality data produces unreliable, biased, or inaccurate models.

Question 4

Why is data representativeness important?

Answer 4

Models fail when real-world scenarios aren’t reflected in training data.

Question 5

How does bias get amplified in ML?

Answer 5

Models encode historical bias and reproduce it at scale.