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Applied GenAI Specialisation Learning Path

Who will benefit

This learning path, provided by our partner Simplilearn, is aimed at working professionals from a variety of industries and backgrounds. It will specifically benefit anyone working in the following roles:

  • Software Engineers
  • IT Professionals
  • Data Professionals
  • Product Managers
  • AI Professionals
  • ML Engineers
  • Data Scientists
  • Aspiring Generative AI
  • Prompt Engineers

Course overview

Master Generative AI with this state-of-the-art program offered by Simplilearn. Delve into prompt engineering, large language models, attention mechanisms, RAG, and LLM fine-tuning. Gain expertise in essential tools and pave the way for the future of intelligent systems.

Course highlights

  • 50-60 hours of instructor-led online classes
  • Access to hands on projects

Key learning outcomes

Python basics

  • Gain proficiency in procedural and object-oriented programming using Python, recognizing its advantages.
  • Install Python and its integrated development environment; familiarize with Jupyter Notebook for practical applications.
  • Understand Python's fundamental concepts, including identifiers, data types, operators, loops, variable scope, indentations, comments, and string functions.

Essentials of Generative AI, Prompt Engineering & ChatGPT

  • Understand the fundamentals, mechanisms, and significance of ChatGPT and generative AI, including explainable AI.
  • Apply prompt engineering techniques and explore diverse applications, use cases, and fine-tuning methods for ChatGPT.
  • Examine the potential of generative AI to revolutionize industries and gain in-depth knowledge of prominent generative AI tools.

Applied Generative AI

  • Gain comprehensive skills in generative models, including LLMs, VAEs, and GANs, and understand their architectures and capabilities.
  • Learn advanced prompt engineering techniques, fine-tuning, and customizing LLMs for specific applications.
  • Understand the critical role of benchmarking in evaluating and comparing the performance of generative AI models.

Generative AI Governance

  • Understand the importance of governance in generative AI, including ethical considerations, regulatory challenges, accountability, and risk mitigation.
  • Learn the ethical foundations of AI, responsible development, bias mitigation, privacy, and setting up AI governance committees.
  • Gain knowledge in identifying and mitigating AI project risks, integrating governance in AI projects, and understanding regulatory trends and career opportunities in AI governance.
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