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It provides functions for descriptive statistics, hypothesistesting, regression analysis, time series analysis, survival analysis, and more. It offers a comprehensive set of built-in statistical functions and packages for hypothesistesting, regression analysis, time series analysis, survival analysis, and more.
AI encompasses various subfields, including Machine Learning (ML), NaturalLanguageProcessing (NLP), robotics, and computer vision. Together, Data Science and AI enable organisations to analyse vast amounts of data efficiently and make informed decisions based on predictive analytics.
DecisionTrees: A supervised learning algorithm that creates a tree-like model of decisions and their possible consequences, used for both classification and regression tasks. Null Hypothesis: A statement that there is no effect or no difference, serving as the basis for statistical testing.
Accordingly, there are many Python libraries which are open-source including Data Manipulation, Data Visualisation, Machine Learning, NaturalLanguageProcessing , Statistics and Mathematics. It includes regression, classification, clustering, decisiontrees, and more. It can be easily ported to multiple platforms.
Concepts such as probability distributions, hypothesistesting , and Bayesian inference enable ML engineers to interpret results, quantify uncertainty, and improve model predictions. DecisionTrees These trees split data into branches based on feature values, providing clear decision rules.
Text Analytics (NaturalLanguageProcessing) Text analytics, also known as naturallanguageprocessing (NLP), involves extracting valuable information and insights from unstructured text data, such as customer reviews, social media posts, or survey responses. Key Features: i.
AI is making a difference in key areas, including automation, languageprocessing, and robotics. NaturalLanguageProcessing: NLP helps machines understand and generate human language, enabling technologies like chatbots and translation.
Statistical analysis and hypothesistesting Statistical methods provide powerful tools for understanding data. Hypothesistesting, correlation, and regression analysis, and distribution analysis are some of the essential statistical tools that data scientists use.
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