<ul><li>Problem Scoping is the crucial first stage of AI project development, utilizing the 4Ws - Who, What, Where, and Why.</li><li>Deep understanding is needed to develop a clear vision for project accomplishment, assisted by the 4Ws Problem Canvas.</li><li>AI solutions support the UN Sustainable Development Goals, aiming to improve lives globally.</li><li>Data Acquisition involves collecting raw data for training AI projects, emphasizing the need for authentic and relevant data.</li><li>Data Exploration involves uncovering patterns and trends in large datasets for AI project planning.</li><li>Data visualization is critical for understanding trends, choosing models, and effective communication.</li><li>Data Modeling focuses on building models with mathematical representations for machine understanding.</li><li>Modeling techniques can be rule-based or learning-based, each with its strengths and limitations.</li><li>Evaluation is crucial for testing the AI model's efficiency and performance using Testing Data.</li><li>Key evaluation metrics include F1 Score to assess the model's reliability and effectiveness.</li></ul>

Introducing AI Project Cycle

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