₦airaland Forum

Machine Learning:

Learns from structured data using algorithms like linear regression and decision trees.
Requires feature engineering (manually selecting relevant data features).
Used for tasks like spam filtering and fraud detection.
Deep Learning:

Mimics the human brain using artificial neural networks with multiple layers.
Can handle raw, unstructured data (images, audio, text) and automatically learns features.
Requires high computational power.
Used for complex tasks like image recognition and natural language processing.
Key Differences:

Data: ML needs structured data, while DL can handle unstructured data.
Feature Engineering: Required for ML, automatic for DL.
Computational Power: DL needs more power than ML.
Interpretability: ML models are easier to understand than DL models.
The choice between ML and DL depends on the specific problem and resources. Both are powerful tools driving AI innovation across various industries.
https://insider-wp.com/machine-learning-vs-deep-learning-mastering-ai-innovation/

Very informative and comprehensive article.
I don't think ANYBODY truly understands AI nor where it's leading