16. What is Supervised Learning?
Supervised learning is a type of machine learning where the model is trained on labeled data, meaning that each training example consists of an input and a known output (label). The goal of the model is to learn a mapping from inputs to outputs, enabling it to make predictions on new, unseen data. Supervised learning is widely used for classification and regression tasks.
17. What is Unsupervised Learning?
Unsupervised learning is a type of machine learning where the model is trained on data that is not labeled. The goal of unsupervised learning is to find hidden patterns or intrinsic structures in the input data. Unlike supervised learning, there are no predefined outputs, and the model learns from the data itself. Unsupervised learning is primarily used for clustering, association, and dimensionality reduction tasks.
18. What is Semi-Supervised Learning?
Semi-Supervised Learning is a type of machine learning that falls between supervised learning (where all data is labeled) and unsupervised learning (where none of the data is labeled). In semi-supervised learning, the model is trained on a small amount of labeled data and a larger amount of unlabeled data. This approach leverages the availability of abundant unlabeled data, which is often easier to collect than labeled data, to improve learning and prediction accuracy.
The key idea is to combine both labeled and unlabeled data, where the labeled data provides specific guidance for learning, and the unlabeled data helps capture the underlying structure of the data distribution.
19. What is Reinforcement Learning?
Reinforcement Learning (RL) is a type of machine learning where an agent learns to make decisions by interacting with an environment. The agent takes actions, and based on the outcome, it receives rewards or penalties (feedback). The goal of the agent is to learn a strategy (or policy) that maximizes cumulative rewards over time. RL is particularly useful in environments where sequential decision-making is needed, and there is no clear labeled data available.
Note: It is a technique that trains software to make decisions to achieve the most optimal results i.e. trial and error.
20. What is Self-supervised learning?
Self-supervised learning is a type of machine learning where the model learns to predict part of the input data from other parts of the data. It generates its own labels from the input data without requiring human-labeled data. The main idea is to create a supervised learning problem from unlabeled data, where the task can be to predict a missing portion of the data, the next frame in a sequence, or context from a given sample. This learning approach is widely used in fields like natural language processing, computer vision, and speech processing.
Short note of Main Types of Machine Learning
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