Types of Machine Learning

Machine Learning is divided into several types based on the learning approach and the type of data available. The main types are:

1. Supervised Learning:
   The model is trained on labeled data, where the input is paired with the correct output. The goal is to learn the mapping from inputs to outputs to predict outcomes for new data.
   Example: Classification and regression tasks like email spam detection or house price prediction.
2. Unsupervised Learning:
   The model is trained on unlabeled data and tries to find hidden patterns or groupings in the data.
   Example: Clustering tasks like customer segmentation or anomaly detection.
3. Semi-Supervised Learning:
   A combination of supervised and unsupervised learning, where a small amount of labeled data is used alongside a larger amount of unlabeled data to improve model accuracy.
   Example: Image recognition where only a few labeled images are available.
4. Reinforcement Learning:
   The model learns by interacting with an environment and receiving feedback through rewards or penalties. The goal is to maximize the cumulative reward over time.
   Example: Self-driving cars, robotics, or game-playing AI.
5. Self-Supervised Learning:
   A type of unsupervised learning where the model generates its own labels from the data and uses those labels for training.
   Example: Predicting the next word in a sentence or learning patterns in large datasets without explicit labels.