Supervised and unsupervised Machine Learning #ai #artificialintelligence #machinelearninglligence

18 May 2025 · 1 min

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1:13

The video provides an introductory overview of how machine learning models are trained from data. It explains that models start with random parameters and require exposure to large datasets to learn patterns. The speaker then outlines the main training paradigms: supervised learning, where models are trained on labeled data linking features to targets; unsupervised learning, where models discover structure in unlabeled data; semi‑supervised learning, which combines a small labeled set with a larger unlabeled set; and reinforcement learning, where an agent learns through interaction with an environment, receiving rewards or penalties similar to biological learning. The explanation is concise and aimed at beginners, emphasizing the role of data and the distinct objectives of each paradigm.

Chapters

Machine learning is all about training from data. This means that at the beginning of the model's construction, it's unable to produce valid predictions because its initial configuration is often random. As a result, it needs to be exposed to a large quantity of data so that it can identify the subtleties and recurrent patterns present in it. This is known as training. In machine learning, there are several types of training, such as: Supervised learning, where the model is trained on labeled data, where each input called feature is associated with a correct output called target. Unsupervised learning, in this case, the model learns from unlabeled data and seeks to identify structures and similarities present in it. Semi-supervised learning, this combines the 2 previous types. In this case, part of the data is labeled, where the larger part is unlabeled. Reinforcement learning, in this type, type of learning, the model learns by interacting with an environment and receiving rewards or penalties. It's a type of learning similar to the mechanism used by living organisms, where learning takes place through success and failure.