Supervised learning: basic concepts
Supervised learning is a machine learning paradigm where the algorithm learns from labeled training data, associating input data with the correct output. It involves the algorithm making predictions based on the patterns it learns from the training dataset. The goal is for the model to generalize its learning to accurately predict outcomes for new, unseen data. In this lecture I give a few examples of supervised learning problem and to discuss what does it mean that a model gives predictions, makes error, and that it learns a dataset.
Coding assignments: https://github.com/ionpetre/FoundML_c...
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