Machine Learning is one of the key branches of artificial intelligence that enables systems to learn and improve from data without explicit programming. In other words, machine learning relates to algorithms and models that can identify patterns and make predictions by analyzing data.

Main Branches

1 . Supervised Learning

In this type of learning, the model is trained with labeled data. In other words, the data includes inputs and corresponding outputs. The model tries to learn the relationship between the input and output.    Example: Predicting house prices based on features such as area, number of bedrooms, and location

2 . Unsupervised Learning

In this case, the data is unlabeled, and the model must identify patterns and structures within the data on its own.

Example: Clustering customers based on purchasing behavior without having a specific label for each group.

3 . Semi-Supervised Learning

A combination of supervised and unsupervised learning. In this approach, only part of the data is labeled, and the model also uses unlabeled data to improve its performance.

4 . Reinforcement Learning

In this type of learning, an agent interacts with its environment and learns to choose the best strategy to achieve its goal by receiving rewards or penalties for its actions.

   Example: Training a robot to play a video game.