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Data Science Clustering use case.

Python programming language and its libraries combined together and R language in addition form the powerful tools for solving Clustering analysis tasks.

Cluster analysis or simply clustering is a branch of machine learning ML which mainly dealt with unsupervised task and usually involves automatically discovering natural grouping in data.

Unlike supervised learning (like predictive modeling), clustering algorithms only interpret the input data and find natural groups or clusters using given features.

In other words Clustering techniques apply when there is no class to be predicted but rather when the instances are to be divided into natural groups - clusters.

https://python-code.pro/clustering-models-python-r-cheatsheets/
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Data Science Association Rule Learning use case.

Python programming language and its libraries combined together and R language in addition form the powerful tools for solving Association Rule Learning tasks.

Association rule learning is a rule-based machine learning ML method for detecting possible relations between variables in large datasets. It's purpose is to identify powerful rules discovered in datasets using some measures of interestingness.

https://python-code.pro/association-rule-learning-python-r-cheatsheets/
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Data Science Reinforcement Learning use case.

Python programming language and its libraries combined together and R language in addition form the powerful tools for solving Reinforcement Learning tasks.

Reinforcement learning - RL - is an branch of machine learning ML which busy with how intelligent agents should take actions in an environment in order to maximize the notion of cumulative reward.

Reinforcement learning is one of three basic machine learning concepts next to supervised learning and unsupervised learning.

Reinforcement learning is distinguished from supervised learning in not needing labelled input/output pairs be presented, and in not needing sub-optimal actions to be explicitly corrected. Instead the focus is on finding a balance between exploration (of uncharted territory) and exploitation (of current knowledge).

https://python-code.pro/reinforcement-learning-python-r-cheatsheets/
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