Machine learning (ML) and artificial intelligence (AI) are closely related yet distinct fields in technology. AI is the broader concept of creating machines that can mimic human intelligence, enabling them to perform tasks such as reasoning, decision-making, problem-solving, and natural language understanding. It encompasses various subfields, including robotics, expert systems, computer vision, and ML.
Machine learning, on the other hand, is a subset of AI focused specifically on the development of algorithms that allow computers to learn patterns and make predictions or decisions without being explicitly programmed. ML relies on data to train models and improve their performance over time through supervised, unsupervised, or reinforcement learning.
The overlap lies in their shared goal of enabling machines to function intelligently. ML serves as a foundational technology for many AI applications. For example, ML techniques are used in natural language processing (a branch of AI), where models are trained to understand and generate human language. Similarly, AI applications like image recognition or recommendation systems heavily rely on ML algorithms for accuracy and efficiency.