Updated: 07 August 2023
- Supervised learning uses labeled datasets. These datasets are designed to train or “supervise” algorithms into classifying data or predicting outcomes. Using labeled inputs and outputs, the model can measure its accuracy and learn. Supervised learning deals with two types of problem: Classification and Regression.
- Unsupervised learning clusters unlabeled data sets. These algorithms discover hidden patterns in data hence they are “unsupervised”. Unsupervised learning models are used for three main tasks: clustering, association and dimensionality reduction
You pass the model some data and the model uses the rules that it inferred from the training to make a prediction, such as, “That data looks like walking,” or “That data looks like biking.”
In traditional programming, your code compiles into a binary that is typically called a program. In ML, the item that you create from the data and labels is called a model.
The goal of training a model is to find a set of weights and biases that have low loss, on average, across all examples.