Types of Machine Learning

 Types of Machine Learning

Machine learning types broadly categorized into the following type:-

1. Supervised Machine Learning
2. Unsupervised Machine Learning
3. Semi-Supervised Machine Learning
4. Reinforcement Learning

1. Supervised Learning: In supervised learning, the model is trained on labeled data, where each data point is associated with a corresponding target or output value. The goal is to learn a mapping between input features and their corresponding labels, enabling the model to predict the correct output for new, unseen inputs.

2. Unsupervised Learning: Unsupervised learning involves training models on unlabeled data, where there are no predefined target values. The objective is to discover patterns, structures, or relationships in the data without explicit guidance. Clustering and dimensionality reduction techniques are common examples of unsupervised learning.

3. Semi-Supervised Learning: Semi-supervised learning combines both labeled and unlabeled data for training. The model learns from the labeled data while utilizing the additional unlabeled data to improve its performance or generalization ability. This approach is useful when acquiring labeled data is expensive or time-consuming.

4. Reinforcement Learning: Reinforcement learning involves an agent learning through interaction with an environment. The agent receives feedback in the form of rewards or penalties based on its actions. The goal is to learn a policy that maximizes the cumulative reward over time. This type of learning is often applied in robotics, gaming, and autonomous systems.