Machine Learning vs. Deep Learning: Key Differences Explained

Machine Learning (ML) and Deep Learning (DL) are both subfields of artificial intelligence, but they differ in complexity and capability.

ML involves training algorithms on data to make predictions or decisions without being explicitly programmed.

It works well with structured data and often requires feature engineering — where humans select the most relevant inputs.

Deep Learning, a subset of ML, uses neural networks with many layers (hence "deep") to automatically learn features from vast amounts of unstructured data like images, text, or audio.

In short: ML is great for simpler, structured tasks; DL excels in handling complex data with less human intervention.