UG Certification in Generative AI for Software Developers

Core Periods Modules

The UG Certification in Generative AI for Software Developers is a well-structured, career-oriented one-year program designed to provide undergraduate students with advanced skills in Generative AI technologies. This program combines foundational academic learning with hands-on practical experience, preparing students for roles in AI-driven software development. The first six months focus on building a strong understanding of key topics such as natural language processing (NLP), deep learning, and the architecture of Generative AI models, including techniques like reinforcement learning. Students will also learn how to integrate Large Language Models (LLMs) into software applications, as well as explore the ethical considerations of using AI technologies. The second phase of the program emphasizes real-world application, where students will apply their knowledge through practical projects, case studies, and problem-solving exercises. They will work on advanced projects, gaining hands-on experience in building and deploying Generative AI-powered applications. Collaboration with peers and industry mentors will help refine their technical and problem-solving skills. Upon successful completion, students will receive the UG Certification in Generative AI for Software Developers, preparing them for career opportunities as AI Software Developers, Machine Learning Engineers, or AI Application Developers in the rapidly evolving field of AI.

1. Period 0

   Generative AI: Introduction and Applications

   Generative AI: Introduction and Applications introduces professionals to the fundamentals of Generative AI, covering key concepts such as neural networks, deep learning, and natural language processing (NLP). The module explores various applications of Generative AI, including content creation, AI-driven design, and language models.

2. Period 1

   Building Generative AI-Powered Applications with Python

   Building Generative AI-Powered Applications with Python teaches professionals how to leverage Python for developing AI-powered applications using Generative AI techniques. The module covers key libraries and frameworks, such as TensorFlow, PyTorch, and Hugging Face, to implement machine learning models and fine-tune large language models (LLMs).

3. Period 2

   Generative AI and LLMs: Architecture and Data Preparation

   Generative AI and LLMs: Architecture and Data Preparation explores the underlying architecture of Generative AI models and Large Language Models (LLMs), providing participants with a deep understanding of how these models are structured and function.

4. Period 3

   Introduction to Deep Learning & Neural Networks with Keras

   Introduction to Deep Learning & Neural Networks with Keras provides professionals with a foundational understanding of deep learning techniques and neural network architectures using Keras. This module covers key concepts such as perceptrons, activation functions, feedforward neural networks, and backpropagation.

5. Period 4

   Gen AI Foundational Models for NLP & Language Understanding

   Gen AI Foundational Models for NLP & Language Understanding focuses on the foundational models used in Generative AI for natural language processing (NLP) and language understanding. This module covers key concepts such as transformers, attention mechanisms, and pre-trained models like GPT and BERT.

6. Period 5

   Industry Work Experience (6 Months), 2 Electives

   The continuation of the work experience allows participants to deepen their expertise and apply their learning in more complex business scenarios. With additional electives, participants refine their understanding.