Let's start AI basics with the ML definition and their types.
Definition from Google ML Introduction course:
ML is the process of training a piece of software, called a model, to make useful predictions or generate content from data.
ML Types:
📍 Supervised Learning. The model is trained on lots of data with existing correct answers. It's "supervised" in the sense that a human gives the ML system data with the known correct results. This type is used for regressions and classifications.
📍 Unsupervised Learning. The model makes predictions using data that does not contain any correct answers. A commonly used unsupervised learning model employs a technique called clustering. The difference from classification is that categories are discovered during training and not defined by a human.
📍Reinforcement Learning. The model make predictions by getting rewards or penalties based the on actions performed. The goal is to find the best strategy to get the most rewards. Approach is used to train robots to execute different tasks.
📍Generative AI. The model creates content (text, images, music, etc.) from a user input. These models learn existing patterns in data with the goal to produce new but similar data.
Each ML type has its own purpose, like making predictions, finding patterns, creating content, or automating routine tasks. Among them, Generative AI is the most popular and well-known today.
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