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AI project cycle

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                           AI project cycle   The 5 key steps of the AI project cycle are  Problem Scoping ,  Data Acquisition ,  Data Exploration ,  Modeling , and  Evaluation , which guide building AI solutions by defining the problem, gathering and understanding data, creating algorithms, and testing their effectiveness, often leading to deployment.   Problem Scoping : Clearly define the problem you're trying to solve, identify project goals, and understand constraints. Data Acquisition : Collect accurate, reliable, and relevant data from various sources to train your AI model. Data Exploration : Analyze and visualize the collected data, arrange it uniformly, and identify patterns or issues before modeling. Modeling : Select and build AI models (e.g., using machine learning algorithms) using the prepared data to find solutions. Evaluation : Test the performance of the developed ...
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Introduction to ai

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                            Introduction to ai Introduction to ai The term ai is coined by Jhon  mccarthy  He defined ai as"the science and engineering of making inteligent machines".    Domains of AI   Natural Language Processing (NLP):  Enables computers to understand, interpret, and generate human language (text/speech), powering chatbots, translation, and sentiment analysis. Computer Vision (CV):  Allows machines to "see" and interpret visual information from the world, used in facial recognition, self-driving cars, and object detection. Data Science/Machine Learning (ML):  Focuses on algorithms that learn from data to find patterns, make predictions, and improve performance without explicit programming, forming the backbone of many AI systems .   SDG's(sustainable development goals) The Sustainable Development Goals (SDGs) are  17 interconnected global goals adopt...
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