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Data distribution in statistics. In a world of statistics a data distribution is a function which presents all the possible values of the data. It also shows how often each value occurs. From this distribution it is possible to calculate the probability of any one particular observation in the sample space, or the likelihood that an observation will have a value which is less than (or greater than) a point of interest. The function of a distribution that presents the density of the values of our data is called a probability density function or simply pdf. https://python-code.pro/visualizing-statictical-distributions-with-python/

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Data Science NLP use case. Python programming language and its libraries combined together and R language in addition form the powerful tools for solving Natural Language Processing tasks. Natural language processing - NLP - is a branch of linguistics, computer science CS and artificial intelligence AI which study the interactions between computers and human language, in fact how to program computers to process and analyze natural language data in large amounts. The main goal is a computer capable of "understanding" the contents of text data, especially the contextual nuances of the language used. The technology must also accurately extract information and basic ideas contained in the documents, categorize and organize the documents themselves. https://python-code.pro/natural-language-processing-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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