Master the creation of advanced generative AI applications using the Langchain framework and Huggingface's cutting-edge models.
Understand the architecture and design patterns for building robust and scalable generative AI systems.
Gain practical experience in deploying generative AI models across various environments, including cloud platforms and on-premise servers.
Explore deployment strategies that ensure scalability, reliability, and optimal performance of AI applications.
Develop Retrieval-Augmented Generation (RAG) pipelines to boost the accuracy and efficiency of generative models by integrating retrieval mechanisms.
Seamlessly incorporate Huggingface's pre-trained models into Langchain applications to leverage their powerful NLP capabilities.
Customize and fine-tune Huggingface models to meet specific application needs and use cases.
Engage in real-world projects demonstrating Generative AI applications in domains such as chatbots, content generation, and data augmentation.
Course Content
Class 01 – Introduction of Generative AI | AI Future Prospects | Career Opportunities | Training Modules | Instructor Introduction | QnA Session
Demo Session
01:58:25
Class 02 – Basics of Python (Punctuations, Mathematical Operators, Brackets and Craces, Logical and Comparison) | Syntax and Semantics | Conditional Statement | Python Variables (Variables, Declaring and Assigning Variables, Naming Conventions, Types) | Practical Examples and Common Errors
Generative AI (Batch-02) – Session 01
02:06:21
Class 03 – Data Types (Integers, Floating, Point Numbers, Strings, Booleans) | Advanced Data Types (Lists, Tuples, Sets, Dictionaries) | Operators (Arithmetic, Comparison, Logical) | Conditional Statements (IF, ELIF, ELSE) | Practical Example – Employees Bonus Calculator
