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Data Science Champion

Build your career in AI, ML & GenAI

Data Science Champion Build your career in AI, ML & GenAIData Science Champion Build your career in AI, ML & GenAIData Science Champion Build your career in AI, ML & GenAI

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Cetrification Courses

Fundamentals of Prompt Engineering

Fundamentals of Prompt Engineering

Fundamentals of Prompt Engineering

 

Unlock the full potential of generative AI with our comprehensive course on Prompt Engineering—a pivotal skill in the modern landscape of language models. Whether you’re a developer, data scientist, or simply curious about harnessing AI's capabilities, this course is designed to equip you with the strategies and techniques needed to master the art of prompting.

What You’ll Learn:

 

  • What is Prompt Engineering & Why we need Prompt Engineering
  • Prompt Engineering Strategy for Large Language Models
  • Prompt Engineering Techniques for Large Language Models
  • Build Chatbot using Prompt Engineering
  • Build Prompt Enginnering based application using OpenAI models
  • In context learning using Prompt Engineering
  • Zero-Shot , One-Shot, Few-Shot Prompt Engineering Techniques
  • Understanding Chain-Of-Thought
  • How to enable reasoning capability of Language Models

Fundamentals of RAG

Fundamentals of Prompt Engineering

Fundamentals of Prompt Engineering

 

Unlock the Power of Generative AI with Retrieval-Augmented Generation (RAG)!

In today’s rapidly evolving AI landscape, traditional language models—no matter how large—face a common limitation: they are bound by the static nature of their training data. As the world changes and new knowledge is created every day, relying solely on pre-trained models can lead to outdated or incomplete answers.

That’s where Retrieval-Augmented Generation (RAG) comes in.

 

What you’ll learn

  • "Why": Why do we even need RAG, and what unique challenges does it address in the world of Generative AI?
  • "What": What exactly is Retrieval-Augmented Generation? I’ll break down its key components and functionality.
  • "How": How to implement RAG in real-world applications.
  • Hands-on: Implement Real World Use Cases using RAG

 

We’ll guide you through two real-world RAG implementations that you can apply and extend in your own projects:

  • LiveStockIQ – A stock market assistant that integrates with real-time financial APIs to provide current stock data, company info, and market trends. You’ll see how retrieval connects to APIs and how LLMs generate insights on top of it.
  • SmartRecruit – An AI-powered recruitment assistant for HR teams that intelligently analyzes resumes and matches them to job descriptions using contextual document retrieval and summarization.


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