Different Types of Machine Learning
In this article, we will explore different types of Machine Learning. The three primary categories of machine learning are supervised learning, unsupervised learning, and reinforcement learning. Different terminologies used in machine learning are-
Agent: A system or entity that senses its surroundings and takes actions to accomplish certain goals or objectives is referred to as an agent in the context of artificial intelligence (AI). Any creature capable of perceiving its surroundings, making decisions, and acting on them can be an agent, including software programs, physical robots, and other living things.
Labeled Data: Labelled data is a dataset where each data point has a matching label or goal value. In other words, a predetermined and known class or category is associated with each data object. The labels include factual information or comments that show the proper categorization or value for each data piece.
Unlabeled Data: When referring to a dataset, the term "unlabeled data" means that the data instances or points do not have associated labels or goal values. Unlabeled data lacks clear annotations or source data, in contrast to labeled data, where each data point is assigned to a specific type or category.
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| Different Types of Machine Learning |
Types of Machine Learning:
- Supervised Learning: Training a model using labeled data, when the desired output is already known, is referred to as supervised learning. Input characteristics are mapped to relevant target labels as the model gains experience. Based on the patterns it has discovered from the labeled instances, it generates predictions. Classification (assigning inputs to predetermined groups) and regression (predicting continuous outcomes) are examples of supervised learning tasks. Decision trees, support vector machines (SVM), random forests, and neural networks are typical supervised learning techniques.
- Unsupervised Learning: In unsupervised learning, the model is trained on unlabeled data without any specified goal labels or acceptable outputs. Finding the data's innate patterns, structures, or correlations is the aim of Unsupervised Learning. Unsupervised learning algorithms seek to detect patterns of connection or correlation, reduce the dimensionality of the data, or locate clusters or groupings of related data points. K-means clustering, hierarchical clustering, principal component analysis (PCA), and autoencoders are a few well-known unsupervised learning methods.
- Reinforcement Learning: Training an agent to interact with an environment and learn from mistakes in order to maximize rewards is known as reinforcement learning. The agent acts in the world, receives feedback in the form of benefits or drawbacks, and then modifies its behavior to maximize its cumulative benefit over time. In situations where an agent learns to play games, operate robots, or make autonomous decisions, reinforcement learning is frequently employed. This sort of learning makes use of algorithms like Q-learning, policy gradients, and deep reinforcement learning (which combines reinforcement learning with deep neural networks).
Note:
These categories are not mutually exclusive, and hybrid strategies are occasionally applicable. For example, transfer learning uses information gained from one task to enhance performance on another, whereas semi-supervised learning includes labeled and unlabeled data.
Semi-Supervised Learning: Semi-supervised learning is a machine learning method that uses labeled and unlabeled data to train models. It incorporates both supervised and unsupervised learning aspects to utilize the sparsely labeled data while benefiting from the wealth of unlabeled data.
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