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Unsupervised learning uses machine learning algorithms to analyze and learns patterns from unlabeled data. These algorithms are able to discover hidden patterns or groupings without the need for human intervention. The ability to discover similarities and differences in unlabeled information make it as one of the main machine learning algorithmic type.
Clustering is one of the main use cases of the unsupervised machine learning approach. Clustering means grouping unlabeled data based on their similarities or differences. Clustering algorithms are commonly used to process raw data objects into groups represented by structures or patterns in the information. There are multiple types of Clustering algorithms available such as exclusive, overlapping, hierarchical, and probabilistic, etc.
Unsupervised learning provides an exploratory path to view data, allowing businesses to identify patterns in large volumes of data more quickly when compared to manual observation. Unsupervised learning techniques are used in recommendation systems, anomaly detection, medical imaging, computer vision, etc. The below image shows the difference between supervised and unsupervised learning.
This is one of the types of machine learning where an agent learns from the environment it is in through reward signals. It is reward-based learning. In other words, it works with the principle of feedbacks. for example, let’s assume we built a model to identify apples. When we give an image of an apple to the model it predicts it as non-apple. then we give negative feedback to the model. The model learns from the negative feedback that the image is apple. This learning will be used to identify apples at another time. This is called Reinforcement Learning.
There are three types of reinforcement learning algorithms called value-Based, policy-based, and model-Based. There are some important characteristics of reinforcement learning. Those are listed below.
- There is no supervisor, only a real number or reward signal
- Sequential decision making
- Agents actions determine its feedback and subsequent data it receives.
- Time plays a crucial role in Reinforcement problems
- Feedback is always delayed, not instantaneous
It is not preferable to use reinforcement learning when we have enough amount of data to solve the problem with other learning methods. As reinforcement learning is heavy in terms of computing and time-consuming, It is not preferable to use it in particular when the action space is too large.
Reinforcement learning is used in many applications such as robotics for industrial automation, aircraft control and robot motion control, data processing, business strategy planning, etc.
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