• Supervised Learning

• Unsupervised Learning

• Reinforcement Learning

• Semi-Supervised Learning

• Self-Supervised Learning

• Multi-Instance Learning

• Inductive Learning

• Deductive Inference

• Transductive Learning

• Multi-Task Learning

• Active Learning

• Online Learning

• Transfer Learning

• Ensemble Learning

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​Now her e we know top and currently used machine learning types:

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1. Supervised Learning: Applications in which the training data comprises examples of the input vectors along with their corresponding target vectors are known as supervised learning problems.

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There are two main types of supervised learning problems: they are classification that involves predicting a class label and regression that involves predicting a numerical value.

• Classification: Supervised learning problem that involves predicting a class label.

• Regression: Supervised learning problem that involves predicting a numerical label.

Both classification and regression problems may have one or more input variables and input variables may be any data type, such as numerical or categorical.

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2. Unsupervised Learning: In unsupervised learning, there is no instructor or teacher, and the algorithm must learn to make sense of the data without this guide.

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There are many types of unsupervised learning, although there are two main problems that are often encountered by a practitioner: they are clustering that involves finding groups in the data and density estimation that involves summarizing the distribution of data.

• Clustering: Unsupervised learning problem that involves finding groups in data.

• Density Estimation: Unsupervised learning problem that involves summarizing the distribution of data.

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Clustering and density estimation may be performed to learn about the patterns in the data.

Additional unsupervised methods may also be used, such as visualization that involves graphing or plotting data in different ways and projection methods that involves reducing the dimensionality of the data.

• Visualization: Unsupervised learning problem that involves creating plots of data.

• Projection: Unsupervised learning problem that involves creating lower-dimensional representations of data.

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The goal in such unsupervised learning problems may be to discover groups of similar examples within the data, where it is called clustering, or to determine the distribution of data within the input space, known as density estimation, or to project the data from a high-dimensional space down to two or three dimensions for the purpose of visualization.

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3. Reinforcement Learning: Reinforcement learning is learning what to do — how to map situations to actions—so as to maximize a numerical reward signal. The learner is not told which actions to take, but instead must discover which actions yield the most reward by trying them.

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Some machine learning algorithms do not just experience a fixed dataset. For example, reinforcement learning algorithms interact with an environment, so there is a feedback loop between the learning system and its experiences.

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In many complex domains, reinforcement learning is the only feasible way to train a program to perform at high levels. For example, in game playing, it is very hard for a human to provide accurate and consistent evaluations of large numbers of positions, which would be needed to train an evaluation function directly from examples. Instead, the program can be told when it has won or lost, and it can use this information to learn an evaluation function that gives reasonably accurate estimates of the probability of winning from any given position.

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