Dive into Advanced Generative AI Course

Mode of Training : Online
Duration: 1.5 Months

Artificial Intelligence | Large Language Models | Prompt Engineering | GPT models | Open AI API | Google Gemini Gen API | Meta’s LLaMA API | Hugging Face Ecosystem | Lang Chain | RAG | Stable Diffusion

  • Lifetime LMS Access
  • 10,000+ Career Transitions
  • 100% Placement Assistance
  • 500+ Hiring Partners for Placements
  • 350+ Batches

Pre-Requisites:
Proficiency in Python, Machine Learning, Deep Learning, and NLP is essential for this course

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Advanced Generative AI

Course Curriculum (Syllabus)

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Module 1: Introduction To Artificial Intelligence

1. Introduction to AI
2. AI vs ML vs DL
3. Types of learning (Supervised, Unsupervised & Reinforcement)
4. Core Difference between ML and DL
5. Life Cycle of ML and DL Project

Module 2: Introduction To Generative AI

1. Introduction to Generative AI
2. Overview of generative AI technologies.
3. Applications and case studies across industries.

Module 3: Getting Started With Large Language Models

1. Into to large language Models
2. History of NLP
3. Intro to RNN,LSTM,GRU
4. Intro to Encoder Decoder Model

Module 4: Prompt Engineering And Working With LLM

1. Intro to Prompt Engineering
2. LLM with Prompt Engineering
3. Introduction to GPT models.
4. Understanding how GPT-3 and GPT-4 work
5. Training on popular LLMs like GPT (Generative Pre-trained Transformer).
6. Practical applications of LLMs in generating text, code, and more
Case Study: Creating a project with LLMS

Module 5: Working with Open AI API

1. Intro To Open Ai
2. Utilizing OpenAI APIs
3. Setting up and authenticating API usage.
4. Practical exercises using GPT-3/GPT-4 for text generation.
5. Understanding DALL-E and its capabilities in image generation.
📜Hands-on project to generate images from textual descriptions.

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Module 6: Working with Google Gemini Gen API

1. Getting Started with Gemini
2. How to obtain an API key for Gemini.
3. Overview of the Gemini API and accessing its features.
4. Detailed exploration of different Gemini models.
5. Selecting and initializing the right model for specific tasks.
6. Step-by-step project to create an AI-powered chatbot using Gemini.

Module 7: Working with Meta’s LLaMA API

1. Introduction of LLaMA .
2. Comparison with other large language models like GPT-3 and GPT-4.
3. Key features and capabilities of LLaMA
4. Understanding the Model Architecture of LLaMA.
5. Discussion on model sizes and capabilities.
6. Environment setup: Installing necessary libraries and tools.7. Intro to the architecture of LLaMA models
8. Understanding the differences between LLaMA model variants (8B, 13B, 30B,
and 70B parameters)
9. Implementing text generation using LLaMA

Module 8: Working With Hugging Face Ecosystem

1. Introduction to the Hugging Face ecosystem and the Transformers library.
2. Exploring Hugging Face Models and Tokenizers.
3. Project: 
4. Introduction to the Trainer API.
5. Integrating Hugging Face models with web application

Module 9: Building Gen AI Apps Using Lang Chain

1. Introduction to the LangChain framework
2. Understanding the purpose and core components of LangChain Framework
3. LangChain Setup and necessary dependencies
4. Basic configuration and setup for development
5. Step-by-step guide 

Module 10: Intro To RAG

1. Intro To RAG
2. Building applications using RAG
3. LLMs in Depth
4. Fine Tuning LLMs
5. Training LLMs by Implementing Fine Tuning

Module 11: Stable Diffusion by Stability AI

1. Intro to Stable Diffusion
2. Fundamentals of Diffusion Models
3. Application of Stable Diffusion
4. Modifying image attributes and styles using prompt engineering
5. Parameters of image generation: seeds, prompts, and steps explained
6. Tool For Stable Diffusion
7. Fine-tuning and training Stable Diffusion on custom datasets
8. Advanced prompt engineering and achieving specific artistic effects.
9. Introduction to variations and derivatives of Stable Diffusion (e.g., DreamBooth
for personalization).
10.Using the Diffusion library for more control over the diffusion process.
11. Integrating Stable Diffusion models into web applications
12.Advance Stable Diffusion Techniques

Generative AI course in hyderabad

Job opportunities (Careers) in Generative AI

 

As data has become the never-ending part of this world, businesses need people to work with data for effective business processing. Organizations are ready to recruit and pay top dollars to the right dollars, which can leverage the business.

Here are some of the roles:

  • GenAI Engineer
  • AI Application Developer
  • Prompt Enginner
  • Machine Learning Engineer
  • AI Research Scientist
  • NLP Engineer
  • Deep Learning Engineer
  • Computer Vision Engineer

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Gen AI