# π”π§ππžπ«π¬π­πšπ§ππ’π§π  𝐭𝐑𝐞 πƒπ’πŸπŸπžπ«πžπ§πœπž 𝐁𝐞𝐭𝐰𝐞𝐞𝐧 π€πˆ 𝐚𝐧𝐝 𝐌𝐚𝐜𝐑𝐒𝐧𝐞 π‹πžπšπ«π§π’π§π  --- --- --- --- rtificial Intelligence) and Machine Learning are often used interchangeably, but they are distinct β€”concepts. AI refers to the broader field of creating machines capable of performing tasks that would β€”typically require human intelligence. Machine Learning, on the other hand, is a subset of AI that focuses on developing algorithms that allow computers to learn from and improve upon data. While AI encompasses a range of technologies, including rule-based systems and robotics, Machine Learning specifically involves training models on data to make predictions or decisions. Understanding this distinction helps clarify how these technologies are applied in real-world scenarios. ![Image](https://upload.cafenono.com/image/slashpagePost/20240801/144722_MSaU9THD2z4CZSoNmR?q=80&s=1280x180&t=outside&f=webp) For the site tree, see the [root Markdown](https://slashpage.com/channel-test.md).