- Supervised learning: Algorithms of supervised learning use labeled data to get trained. The models take direct feedback to confirm whether the output that is being predicted is, indeed, correct. Moreover, both the input data and the output data are provided to the model, and the main aim here is to train the model to predict the output when it receives new data. It can largely be divided into two parts, classification and regression. It offers accurate results.
- Unsupervised learning: Unsupervised learning algorithms use unlabeled data for training purposes. In this, the models do not take any feedback, and unlike the case of supervised learning, these models identify hidden data trends. The unsupervised learning model is only provided with the input data, and its main aim is to identify hidden patterns to extract information from the unknown sets of data. It can also be classified into two parts, namely, clustering and associations. Unfortunately, unsupervised learning offers results that are comparatively less accurate.