Supervised Learning
In supervised learning, the algorithm is trained on a label dataset,
where the input data and corresponding output data are provided. The algorithm learns to
make predictions by generalizing from the labeled examples. It aims to minimize the
difference between its predictions and the actual output. Examples of supervised learning
include image classification, speech recognition, and fraud detection
Unsupervised Learning
In unsupervised learning, the algorithm is trained on an unlabeled
dataset, where the input data is provided, but the corresponding output data is not. The
algorithm learns to find patterns or structures in the data by clustering or grouping
similar data points together. It aims to find hidden patterns or relationships in the data.
Examples of unsupervised
learning include customer segmentation, anomaly detection, and dimensionality reduction.
Reinforcement learning
In this type of learning, the algorithm learns by interacting with an
environment and receiving feedback in the form of rewards or punishments.
The algorithm learns to take actions that maximize the reward it receives over time
There are also some other sub-types of machine learning, such as
semi-supervised learning (a combination of supervised and unsupervised learning),transfer
learning (where knowledge learned from one task is applied to another), and deep learning
(a type of machine learning that uses artificial neural networks to learn hierarchical
representations of data).
