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Course Overview
The Generative AI for Developers course is a hands-on, in-depth program designed to equip developers with the skills and knowledge required to build and implement advanced AI applications. Covering a range of foundational and advanced topics, from prompt engineering to deploying AI agents, this course provides a comprehensive understanding of the Generative AI ecosystem.
Tools Used
- Programming Environment: VS Code, Python 3.11 or above, Google Colab
- AI Platforms: Google Vertex AI, OpenAI, Azure OpenAI, Hugging Face, Groq API, Together AI, promptfoo
- Specialized Libraries: LangChain, PineCone, Chroma
Modules
Deep Dive into Generative AI Tasks
Tools and Platforms for Generative AI
Enterprise Applications of Generative AI
Introduction to AI Agents
Fundamentals of Prompt Engineering
Advanced Prompting Techniques
Multimodal Prompting Strategies
Implementing AI Agents with LangChain
Tool Integration with LangChain Agents
Introduction to Embeddings in AI
Advanced Embedding Techniques
Retrieval Augmented Generation (RAG) Overview
Advanced Retrieval Techniques and Multimodal RAG
Testing and Deploying AI Agents
Capstone Project
Introduction to Generative AI Concepts
Key Topics:
- Overview of Generative AI vs. Discriminative AI
- Core principles and historical evolution of AI
- Basics of Large Language Models (LLMs) and Transformer Models
Virtual Lab:
- Lab 1A: Exploring Generative AI Models
– Hands-on exploration of GPT and transformer-based models - Lab 1B: Basic Text and Image Generation with Hugging Face
– Setting up Hugging Face models for text generation
Deep Dive into Generative AI Tasks
Key Topics:
- Key tasks: Text generation, Image generation, Translation
- Foundations of GPT, BERT, Diffusion Models
- Understanding Generative AI in practical scenarios
Virtual Lab:
- Lab 2A: Text Generation with GPT
– Creating and tuning text generation models for simple tasks - Lab 2B: Basics of Image Generation
– Introduction to diffusion models for image generation
Tools and Platforms for Generative AI
Key Topics:
- Exploring popular platforms (Hugging Face, OpenAI)
- Comparing LLMs across vendors (OpenAI, Cohere, Google)
- Metrics for evaluating LLM efficiency and choosing optimal models
Virtual Lab:
- Lab 3: Accessing and Comparing LLMs
– Using Hugging Face and OpenAI models and comparing output, speed, and accuracy across LLMs
Enterprise Applications of Generative AI
Key Topics:
- Architectures and frameworks for LLM-powered applications
- Analyzing real-world case studies in various industries
- Integrating Generative AI into existing enterprise systems
Virtual Lab:
- Lab 4: Building Generative Models for Enterprise Applications
– Developing a simple enterprise chatbot prototype with LLM integration
Introduction to AI Agents
Key Topics:
- Overview of AI agents, from simple automations to complex systems
- Generative AI Agents for content generation, creative arts
- Tools and platforms for AI agent development
Virtual Lab
- Lab 5: Implementing AutoGPT with Synapse CoR
– Building and configuring a basic automation agent with AutoGPT for predefined tasks
Fundamentals of Prompt Engineering
Key Topics:
- Introduction to prompt engineering, key concepts
- Zero-shot, Few-shot, and Chain of Thought Prompting
- Practical strategies for crafting effective prompts
Virtual Lab:
- Lab 6A: Zero-shot and Few-shot Prompting
– Crafting prompts with minimal examples and adjusting for accuracy - Lab 6B: Chain of Thought Prompting for Text Analysis
– Building step-by-step reasoning prompts for more complex queries
Advanced Prompting Techniques
Key Topics:
- Tree of Thought Prompting: Scenarios and Applications
- ReAct and CoVe Prompting for enhanced LLM responses
- Use cases for coding, NLP, and vision tasks
Virtual Lab:
- Lab 7: Implementing Advanced Prompting Patterns
– Using Tree of Thought and CoVe prompting for various industry-related tasks
Multimodal Prompting Strategies
Key Topics:
- Prompting for text, image, and video generation tasks
- Task-specific prompt optimization methods
- Case studies for multimodal Generative AI applications
Virtual Lab:
- Lab 8: Multimodal Prompt Engineering
– Creating prompts for text-to-image and text-to-video generation applications
Implementing AI Agents with LangChain
Key Topics:
- Introduction to LangChain, LlamaIndex, and Enterprise Scenarios
- Creating LangChain-based agents from inception to deployment
- Integrating Azure OpenAI for enriched language processing
Virtual Lab:
- Lab 9: Developing AI Agent with LangChain
– Building a basic LangChain agent and testing its functionality with sample tasks
Tool Integration with LangChain Agents
Key Topics:
- Overview of tool libraries and custom integrations
- Building custom agents and tools with LangChain
- Implementing bespoke tools for tailored enterprise solutions
Virtual Lab:
- Lab 10: Custom Tool Integration with LangChain
– Integrating custom tools into LangChain agents for industry-specific applications
Introduction to Embeddings in AI
Key Topics:
- Overview of embeddings, Word2Vec, and GloVe
- Text embedding applications in NLP and code analysis
- Visualizing embeddings with t-SNE and PCA for analysis
Virtual Lab:
- Lab 11A: Implementing Word2Vec and GloVe
– Building and analyzing basic word embeddings - Lab 11B: Text Embedding Visualization
– Using t-SNE to visualize word relationships in embeddings
Advanced Embedding Techniques
Key Topics:
- Vector retrieval concepts, dimensionality reduction methods
- Introduction to vector databases (PineCone, Chroma)
- CRUD operations and data retrieval techniques
Virtual Lab:
Lab 12: Vector Database with PineCone
– Performing CRUD operations and data retrieval with PineCone for fast text matching
Retrieval Augmented Generation (RAG) Overview
Key Topics:
- Basics of RAG, integrating LangChain with OpenAI
- Building RAG solutions for enterprise chat applications
- Case studies in RAG applications for industry use
Virtual Lab:
- Lab 13: Implementing RAG for Enterprise Solutions
– Creating a domain-specific chat solution using LangChain and RAG methods
Advanced Retrieval Techniques and Multimodal RAG
Key Topics:
- Multimodal RAG: Combining text, image, and other data
- Advanced retrieval techniques like semantic hashing
- Implementation of multimodal solutions with LangChain
Virtual Lab:
- Lab 14: Multimodal RAG Implementation
– Combining text and image data retrieval for a multimodal Q&A application
Testing and Deploying AI Agents
Key Topics:
- Testing AI agents: unit, integration, performance
- Monitoring AI agents with LangSmith for LLMOps
- Security and best practices in deploying AI agents
Virtual Lab:
- Lab 15: Monitoring and Testing with LangSmith
– Implementing LangSmith for end-to-end monitoring and evaluation of an AI agent
Capstone Project
Key Topics:
- Developing an end-to-end Generative AI Agent Solution
- Using LangChain, RAG, embedding databases
- Deploying AI solution with industry-focused application
Virtual Lab:
Capstone Lab: Full Deployment of AI Agent
– Complete development and deployment of a customized AI agent for real-world use case
Learning Outcomes
- Understand and differentiate Generative AI models, including GPT, BERT, and Diffusion Models.
- Develop and deploy AI agents using advanced prompting techniques and LangChain.
- Integrate multimodal AI solutions combining text, image, and video generation.
- Utilize embeddings and vector databases for enhanced AI performance.
- Implement Retrieval Augmented Generation (RAG) and advanced retrieval techniques.
- Deploy and monitor AI agents in real-world applications, ensuring robust performance and security.
Fees & Enrollment
- Course Fee: INR 49,999
Career Services
- 100 % Job Placement Support
- Interview Practice and Preparation
- 1 on 1 Career Mentoring Sessions
- Career Development Workshops
- Resume and LinkedIn Profile Enhancement
Generative AI Course for Developers
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Course Duration: 8 weeks
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Course Format: Online Interactive Sessions with Virtual Lab Components
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Weekly Schedule: 2-hour sessions, held twice on weekdays or weekends
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Certification: Globally Recognized Generative AI for Developers
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You should have a basic understanding of Python. Familiarity with basic machine learning concepts is helpful, but we’ll provide guidance on any advanced topics you’ll need along the way.
In addition to live sessions with instructors, you’ll have access to a course support team for any questions. We also offer a community discussion board where you can connect with other participants, share insights, and seek help.
You’ll be assessed through a combination of lab activities, module quizzes, and a final capstone project. These assessments are designed to ensure you have a solid understanding of each topic before moving forward.
The course includes multiple labs, such as building AI agents, implementing RAG solutions, and integrating custom tools. The capstone project will involve developing a full Generative AI solution, allowing you to apply what you’ve learned in a real-world scenario.
Absolutely. The course includes collaborative lab sessions and a dedicated discussion board where you can interact with other developers, share knowledge, and build connections in the AI community.
Yes, while the course primarily focuses on practical applications, we also address ethical and responsible AI practices, especially in the context of deploying AI agents and handling data.
Yes, the course includes modules on deployment strategies, monitoring, and testing AI models in production environments, using tools like LangSmith for LLMOps.
The course is structured to be completed in 8 weeks with a recommended pace, but you’ll have additional time to finish any pending assignments or the capstone project if needed.
Yes, Generative AI is a rapidly growing field, and the skills you gain here can make you a valuable asset to companies looking to leverage AI. You’ll gain practical, in-demand skills that are applicable across multiple industries.
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