Gen AI Course

AI Voice Specialist

Prompt Engineer

AI Chatbot Developer

LLM Engineer

AI Research Scientist

Data Engineer

Synthetic DataEngineer

Gen AI Developer

ML Ops Engineer

AI Content Creator

Data Scientist

Generative AI Consultant

Employment in AI-related Roles

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Learners will secure roles such as AI Developer, Data Scientist, Machine Learning Engineer, etc.

Industry-Specific Expertise

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Learners will specialize in AI applications across multiple industries like healthcare, finance, and retail etc

Higher Earning Potential

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Learners will have access to higher-paying roles in the tech sector, with potential for rapid salary growth.

Advanced Research Opportunities

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Learners will be well-equipped for academic or corporate research roles in cutting-edge AI and machine learning technologies.

Entrepreneurship in AI

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Learners will be capable of launching AI-focused startups or working as independent consultants in AI.

Gen AI Course

  1. Introduction to Generative AI (1 hours)
  2. Types of Generative Models and their Usage(2 hours)
    • Transformers (BERT, GPT)
  3. Overview On Various LLM Services & Providers (10 hours)
    • Huggingface (Opensource LLMs)
    • Ollama (Localhost LLMs)
  4. Prompt Engineering (5 hours)
    • Fundamentals of Prompt Engineering
    • Core Concepts and Syntax
    • Crafting Prompts: Core Techniques
  5. Python for Generative AI (10 hours)
    • Fundamentals of Python for AI
    • Python Essentials for AI Development
    • Natural Language Processing with Python
    • Generating Images with Python
  6. LLM application frameworks (10 hours)
    • Lang Chain
    • Llama Index
  7. Industry wide Use Cases (5 hours)
    • Text Generation
    • Text Classification and Understanding
  8. Retrieval-Augmented Generation (RAG) (9 hours)
    • RAG Explained
    • Building a RAG setup
    • RAG advantages
    • Rag use cases
    • Hands on activities
  9. Capstone Project (8 hours)
  1. Introduction to Generative AI (1 hours)
  2. Types of Generative Models and their Usage(3 hours)
    • Transformers (BERT, GPT)
    • Diffusion Models
  3. Overview On Various LLM Services & Providers (10 hours)
    • Huggingface (Opensource LLMs)
    • Ollama (Localhost LLMs)
  4. Prompt Engineering (10 hours)
    • Fundamentals of Prompt Engineering
    • Core Concepts and Syntax
    • Crafting Prompts: Core Techniques
    • Optimization Strategies
  5. Python for Generative AI (12 hours)
    • Fundamentals of Python for AI
    • Python Essentials for AI Development
    • Natural Language Processing with Python
    • Generating Images with Python
  6. LLM application frameworks (10 hours)
    • Lang Chain
    • Llama Index
  7. Industry wide Use Cases (25 hours)
    • Text Generation
    • Text Classification and Understanding
    • Language Translation, Summarization, and Conversational Agents
    • Code Understanding and Generation
    • Information Retrieval and Search
  8. Retrieval-Augmented Generation (RAG) (9 hours)
    • RAG Explained
    • Building a RAG setup
    • RAG advantages
    • Rag use cases
    • Hands on activities
  9. Capstone Project (10 hours)
  1. Introduction to Generative AI (1 hours)
  2. Types of Generative Models and their Usage(5 hours)
    • Transformers (BERT, GPT)
    • Diffusion Models
    • GANs (Generative Adversarial Networks)
    • VAEs (Variational Autoencoders)
  3. Overview On Various LLM Services & Providers (16 hours)
    • Google Cloud Platform
    • OpenAI
    • Amazon Web Services
    • Huggingface (Opensource LLMs)
    • Ollama (Localhost LLMs)
    • Grok 2 – X.Ai (Chat)
  4. Prompt Engineering (10 hours)
    • Fundamentals of Prompt Engineering
    • Core Concepts and Syntax
    • Crafting Prompts: Core Techniques
    • Optimization Strategies
  5. Python for Generative AI (12 hours)
    • Fundamentals of Python for AI
    • Python Essentials for AI Development
    • Natural Language Processing with Python
    • Generating Images with Python
  6. LLM application frameworks (10 hours)
    • Lang Chain
    • Llama Index
  7. Industry wide Use Cases (33 hours)
    • Text Generation
    • Text Classification and Understanding
    • Language Translation, Summarization, and Conversational Agents
    • Code Understanding and Generation
    • Information Retrieval and Search
    • Conversational AI and Dialogue Systems
    • Personalization and Recommendation Systems
    • Knowledge Management and Data Mining
    • Creative Content Creation
    • Data Augmentation and Synthetic Data Generation
  8. Retrieval-Augmented Generation (RAG) (11 hours)
    • RAG Explained
    • Building a RAG setup
    • RAG advantages
    • Rag use cases
    • Hands on activities
  9. Prompt Engineering vs RAG vs Fine-Tuning (1 hours)
  10. Safe GenAI (1 hours)
  11. Capstone Project (20 hours)

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