What will you learn in Generative AI Essentials Course
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Fundamentals of generative AI & its history: Learn core concepts, development timeline, and neural model basics.
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Building and scaling AI models: Understand pretraining, fine-tuning, LLM deployment, and multimodal model approaches.
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Vision & audio generation techniques: Explore architectures for vision transformers, masked image modeling, and audio generation.
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Prompting strategies & AI tool communication: Learn effective prompt techniques to guide generative AI outputs.
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Ethics, risks & responsible use: Gain awareness of bias, deepfake risks, and regulation—practicing safe, ethical GenAI use.
Program Overview
Module 1: Introduction to Generative AI
⏳ ~1 hour
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Topics: Definitions, history, differences between generative and traditional AI.
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Hands-on: Interactive explanations and concise quizzes to check comprehension.
Module 2: Training & Scaling Models
⏳ ~1.5 hours
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Topics: Pretraining, fine‑tuning pipelines, foundation models, large-scale LLM architectures.
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Hands-on: Analyze and compare model components and deployment scenarios.
Module 3: Text, Image & Audio Generation
⏳ ~2 hours
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Topics: LLM text generation methods, vision transformer workflows, masked image modeling, and audio generation strategies.
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Hands-on: Walkthroughs using case studies (e.g., vision models & text-to-image pipelines).
Module 4: Prompting & AI Communication
⏳ ~1 hour
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Topics: Prompt engineering fundamentals and context-responsive prompting.
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Hands-on: Practice crafting effective prompts for different AI modalities.
Module 5: Ethics, Safety & Responsible Use
⏳ ~1 hour
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Topics: AI bias, deepfake and misinformation threats, environmental and regulatory concerns.
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Hands-on: Identify potential risks and design mitigation strategies across case examples.
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Job Outlook
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In-demand skillset: Generative AI expertise is increasingly essential for roles in AI engineering, product development, and research.
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Career benefits: Enables contribution to teams focused on LLMs, multimodal systems, content AI, and ethical AI operations.
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Future-proofing: Builds core knowledge of scalable models and responsible AI use—valuable across all industries.
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Freelance & consulting: Opens possibilities in prompt design, model fine-tuning, bias auditing, and ethical advisory work.
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