Tausif Tech Hub

Types of Machine Learning

Machine Learning is a branch of Artificial Intelligence (AI) that enables systems to learn from data and make decisions without being explicitly programmed. It is mainly classified into three types, based on how the model learns from the data:

1. Supervised Learning:

   • In this type, the model is trained on a labeled dataset, meaning both input and output are provided.

   • The goal is to learn a mapping from inputs to outputs.

   • Examples: Email spam detection, house price prediction, sentiment analysis.

   • Algorithms: Linear Regression, Decision Trees, Support Vector Machines.

2. Unsupervised Learning:

   • Here, the model is given input data without labeled outputs.

   • It tries to find hidden patterns, groupings, or structures in the data.

   • Examples: Customer segmentation, market basket analysis.

   • Algorithms: K-Means Clustering, PCA, Hierarchical Clustering.

3. Reinforcement Learning:

   • The model learns by interacting with an environment and receiving rewards or penalties.

   • It is used for decision-making in dynamic situations.

   • Examples: Game playing (like chess), robotics, self-driving cars.

   • Algorithms: Q-learning, Deep Q Networks.

Conclusion:
These three types enable machines to learn and adapt in different environments, solving a wide range of real-world problems.