Machine learning is a subset of artificial intelligence that involves the use of algorithms and statistical tools to identify hidden insights from data. It is based on the idea that systems can learn from data, identify patterns, and make decisions with minimal human intervention.
There are several types of machine learning, including:
Supervised learning:
In supervised learning model is train on labeled data, where the correct output is provided for each example in the training set. The model makes predictions based on this input-output mapping.
Some examples of supervised learning tasks include:
- Classification: Predicting which category an input belongs to (e.g., spam/not spam, male/female).
- Regression: Predicting a continuous value (e.g., price of a house, length of a document).
Unsupervised learning:
This involves training a model on unlabeled data, where the model must discover the underlying structure or patterns in the data.
Semi-supervised learning:
Semi-supervised learning model based on a combination of labeled and unlabeled data.
Reinforcement learning:
This involves training a model to take a series of actions in an environment in order to maximize a reward
