AI vs. Machine Learning vs. Deep Learning: What’s the Difference?

Definition of AI: The broad concept of machines mimicking human intelligence.

Machine Learning (ML): AI's subset, learning from data without explicit programming.

Deep Learning (DL): A type of ML using neural networks with multiple layers.

Purpose: AI for general intelligence, ML for data insights, DL for complex data.

Use Cases: ML for predictions, DL for tasks like image recognition.

Complexity: DL models are computationally more intensive than ML.

Data Requirements: DL requires large datasets; ML can work with smaller sets.

Training Process: ML models are trained on labeled data; DL on big data.

Applications: AI broadly used, ML in business, DL in advanced fields.