Generative AI Specialist (Batch-03)

Categories: Artificial Intelligence AI

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

Introductory Class

Generative AI (Batch-03) – Introductory Class

02:02:47

Class 01 – Python: Getting Started with Python and VS Code | Setting Up Python and VS Code | Introduction to Variables and Basic Data Types | Exploring Integer | Float | String | Boolean | and List Data Types | Using Print Statements | Writing Comments in Python

Class 02 – Python Basics – Data Types and Control Flow | Using the type() Function and Type Conversions | Handling User Input with input() Function | Writing Conditional Statements (if | elif | else)

Class 03 – Python Loops and Functions | For Loops | While Loops | Applying Useful Functions on Various Data Types

Class 04 – Advanced Python Data Structures | Advanced List Data Types and Methods | Introduction to Dictionaries and Their Usage

Class 05 –

Class 06 –

Class 07 –

A course by

Manager Data Science Unit - AVP

Proficiency in Python programming, including its basic syntax, data structures, and functions.
A fundamental understanding of machine learning concepts, covering both supervised and unsupervised learning, as well as core AI principles.
Basic familiarity with deep learning concepts and frameworks (such as TensorFlow or PyTorch) is beneficial, though not mandatory.
Knowledge of APIs and how to interact with them, as the course will include integrating various APIs for model deployment and implementation.
Ability to navigate and execute commands within a command-line interface (CLI) or terminal.