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IQAC
Composition of IQAC
IQAC_policy
Comparative Data

 

FeatureArtificial Intelligence (AI)Machine Learning (ML)Deep Learning (DL)Data Science (DS)Data Analytics (DA)
DefinitionMachines acting to simulate human intelligence.Subset of AI focusing on algorithms that learn from data.Subset of ML using multi-layered neural networks.Multidisciplinary field extracting insights from data.The process of analyzing raw data to make decisions.
Core GoalTo create intelligent systems/machines.To improve performance/predict without explicit programming.To learn complex patterns in large datasets automatically.To derive actionable insights from data to guide strategy.To find specific answers to business questions (What/Why).
ScopeBroadest (umbrella term).Subset of AI.Subset of ML (subset of AI).Very Broad (covers Data prep, DS, ML, DL).Focused (subset of Data Science).
Data TypeStructured/Unstructured.Primarily Structured.Unstructured (Image/Audio/Text).Structured and Unstructured.Primarily Structured (historical).
ApproachLogic-based, rules, or algorithms.Statistical Models/Algorithms.Artificial Neural Networks (ANNs).Statistical methods, math, and coding.Data cleaning, EDA, & Visualization.
Key ToolsIBM Watson, Google AI.Scikit-learn, TensorFlow.Keras, PyTorch, TensorFlow.Python, R, SQL, Tableau, SAS.Excel, SQL, Tableau, Power BI.
ExampleSelf-driving cars, Chatbots.Fraud detection, Spam filters.Facial recognition, Speech-to-Text.Customer behavior analysis.Sales trend analysis.