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Learning Outcomes Upon successful completion of this program, learners will be able to: Explain Core AI Concepts Define artificial intelligence (AI), machine learning (ML), and deep learning (DL). Distinguish between supervised, unsupervised, and reinforcement learning. Identify real-world business applications of AI technologies. Evaluate AI’s Business Impact Analyze how AI transforms industries (e.g., finance, healthcare, marketing). Assess ethical considerations, biases, and risks in AI adoption. Discuss AI’s role in automation, decision-making, and customer experience. Apply AI Tools for Business Solutions Use no-code/low-code AI platforms (e.g., Google AutoML, IBM Watson) to prototype solutions. Interpret data insights from AI-driven analytics tools. Design a basic AI strategy for a business case study. Understand Data Fundamentals Describe the importance of data quality, preprocessing, and labeling for AI models. Identify common data sources and governance frameworks for AI projects. Communicate AI Value to Stakeholders Translate technical AI concepts into business benefits for non-technical audiences. Justify AI investments using ROI and key performance indicators (KPIs). Navigate AI Trends and Limitations Recognize emerging trends (e.g., generative AI, NLP) and their business relevance. Acknowledge current limitations of AI (e.g., interpretability, scalability).