Types of Machine Learning- Supervised, Unsupervised and Reinforcement Learning
Machine Learning is a technique to implement Artificial Intelligence that can learn from the data without being explicitly programmed.
There are three types of Machine Learning:
1. Supervised Learning
In this method, a model is trained on a labeled dataset, which means that each input in the training data is associated with a corresponding output or label. Example: suppose there is a dataset regarding pricing of houses. The prices of houses are labeled (with an output), indicating a relationship with the number of bedrooms and bathrooms. Our objective is to train a model using this labeled data so that, when faced with a new scenario involving a house with, for instance, 5 bedrooms and 3 bathrooms or any other combination, the model can make predictions regarding the probable price of the house.
2. Unsupervised Learning
In this method, the algorithm is given data without explicit instructions. The goal is for the algorithm to discover the patterns, relationships, or structure within the data on its own. Unlike supervised learning, there are no labeled outputs or target variables provided during the training process. Example: Suppose there is a dataset which contains customer related information and their purchase behaviour. In this dataset we have age, gender, transaction amount and the product preference but no fixed output or labelled data. Now, the objective of our model is to find the underlying patterns, create relationships and segregate(or cluster) those customers. These segments can provide valuable insights into customer behavior, preferences, and demographics, enabling businesses to improve customer engagement, and optimize decision-making without relying on predefined categories or labels.
3. Reinforcement Learning:
A type of machine learning approach where an agent learns to make decisions by interacting with an environment. The agent takes actions in the environment, and based on those actions, it receives feedback in the form of rewards or penalties. This is the way the machine learns.
Reinforcement learning is particularly well-suited for sequential decision-making problems where the consequences of actions unfold over a series of steps, and the agent must learn to balance exploration (trying new actions) and exploitation (choosing actions with known positive outcomes).
Congratulations!! Now, you have a good idea of types of Machine Learning. Happy Learning.. :)
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