ML is like teaching our robot how to learn from experience. Instead of programming it with every single piece of knowledge and rule, we use machine learning. It's a subset of AI that focuses on building systems that learn from data. By feeding our robot lots of examples (data), it starts to learn patterns or rules on its own. For instance, if we show it many pictures of cats and dogs, over time, it learns to tell them apart. It's about giving the machine access to data and letting it learn for itself.
How ML is Different from AI:
Scope: AI is broader, aiming to create machines that can mimic human intelligence in general. ML is a specific approach within AI focusing on learning from data.
Function: AI includes reasoning, problem-solving, and learning. ML specifically refers to the learning part, where machines improve from access to data.
Goal: The goal of AI is to create systems that can perform any cognitive task. ML's goal is more focused on making predictions or decisions based on data.
In simple terms, you can think of AI as the quest to make smart machines, while ML is a way of achieving that by teaching
What is Machine Learning (ML)
Machine learning (ML) and artificial intelligence (AI) are two terms that often come together but mean different things. Let's simplify these concepts, imagine you have a smart robot. The goal of making this robot "intelligent" involves both AI and ML but in different ways.