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Generative AI and Agentic AI Course

This course introduces learners to the foundations and real-world applications of Generative AI, covering large language models, prompt engineering, text, image, and code generation. Participants gain hands-on experience building and deploying generative AI solutions while understanding ethical considerations, limitations, and business use cases across industries.

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Generative AI and Agentic AI Course

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  1. Revolutionizing Technology and Business Generative AI

  2. Generative AI is transforming the digital landscape by enabling machines to create content autonomously. This technology leverages advanced algorithms to produce text, images, and other media, opening new opportunities for innovation. Understanding its core principles and applications is crucial for businesses aiming to stay competitive in a rapidly evolving market. Introduction

  3. Generative AI refers to artificial intelligence systems designed to generate new data similar to existing data. It uses models like neural networks to create text, images, audio, or code. Key concepts include machine learning, deep learning, and neural networks, which enable AI to learn patterns and produce meaningful outputs autonomously. Definition and Key Concepts

  4. Generative AI is widely applied in sectors such as marketing, entertainment, healthcare, and software development. It powers tools for content creation, personalized recommendations, drug discovery, and automated coding. Businesses leverage this technology to enhance productivity, innovate services, and improve customer engagement through automated creative processes. Applications Across Industries

  5. Generative AI presents unique challenges including data privacy, bias, and misinformation. Ensuring ethical use requires transparency, accountability, and robust regulatory frameworks. Addressing these issues is essential to mitigate risks such as algorithmic bias and misuse of content, while fostering trust in AI systems across industries. Challenges and Ethical Considerations

  6. Agentic AI systems possess autonomy, decision-making capabilities, and goal-oriented behavior. They can operate independently, adapt to new situations, and improve their performance over time. This type of AI mimics human agency, enabling more sophisticated and flexible interactions with complex environments. Characteristics of Agentic AI

  7. Recent advances in agentic AI include reinforcement learning, self-supervised learning, and multi-agent systems. Innovations focus on enhanced autonomy, improved reasoning, and ethical frameworks for safe deployment. These trends drive progress in sectors like robotics, autonomous vehicles, and intelligent decision support. Current Trends and Innovations

  8. Agentic AI is expected to revolutionize industries by enabling smarter automation, personalized services, and enhanced problem-solving. Its impact will extend to workforce transformation, economic growth, and ethical governance. Preparing for these changes involves embracing interdisciplinary collaboration and proactive policy development. Future Prospects and Impacts

  9. Generative and agentic AI represent transformative technologies with significant potential and challenges. Ethical considerations and continuous innovation are critical to harness their benefits responsibly. Organizations must stay informed and adaptable to leverage AI’s evolving capabilities for sustainable success. Conclusions

  10. THANKS

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