Definition of ML and why it matters in the modern world.
Machine Learning (ML) is a subset of artificial intelligence (AI) that provides systems the ability to automatically learn and improve from experience without being explicitly programmed. Coined by Arthur Samuel in 1959, the core idea is to build algorithms that can receive input data and use statistical analysis to predict an output value within an acceptable range. Unlike traditional programming, where a developer writes explicit rules for a program to follow, ML algorithms are designed to learn these rules on their own from large amounts of data. For instance, instead of writing complex rules to identify a cat in a photo, you would show an ML model thousands of cat photos, and it would learn the features that define a cat. This data-driven approach is what makes ML so powerful. It allows us to tackle complex problems that are difficult or impossible to solve with traditional methods, such as natural language processing, image recognition, and medical diagnosis. In essence, ML is about creating predictive models; the algorithm is the engine that finds the patterns, and the data is the fuel that powers it. This shift from rule-based logic to data-driven insights is the reason ML is at the heart of so many technological advancements today.