Machine learning (ML) and deep learning (DL) are two major subsets of artificial intelligence (AI) that are often used interchangeably. However, they have distinct differences in structure, capabilities, and applications.
What is Machine Learning?
Machine learning is a branch of AI that enables computers to learn from data and make predictions or decisions without being explicitly programmed. ML algorithms identify patterns in data and improve their performance over time. Common types of ML include:
Supervised Learning: Uses labeled data to train models (e.g., spam detection in emails).
Unsupervised Learning: Identifies patterns in unlabeled data (e.g., customer segmentation).
Reinforcement Learning: Trains models based on rewards and penalties (e.g., game-playing AI).
ML models typically rely on structured data and require human intervention for feature selection and optimization.
What is Deep Learning?
Deep learning is a subset of ML that uses artificial neural networks (ANNs) to simulate human brain functions. It processes large amounts of unstructured data (such as images, audio, and text) through multiple layers of neurons. DL models are commonly used in:
Computer Vision (e.g., facial recognition)
Natural Language Processing (e.g., chatbots, translation)
Autonomous Vehicles (e.g., self-driving cars)
Deep learning requires high computational power and large datasets but achieves superior accuracy compared to traditional ML models.
