Generative AI

Course Name Course Fees Time StartDate Enroll
Generative AI 15000 INR 7.30 – 9 PM/7.00 – 9 AM (Sat,Sun) Weekend Classes – April 13 Enroll Now

Overview

Generative AI course provides a comprehensive introduction to generative modeling and its applications in machine learning. Students will gain theoretical and practical knowledge of the major approaches for developing generative models, including variational autoencoders, generative adversarial networks, autoregressive models, energy-based models, and diffusion models. Through hands-on programming assignments, students will implement key architectures for tasks such as text, image, and audio generation. The course will also cover important topics relating to evaluation, bias detection, controllability, and multimodal generation.

Objectives

  • Gain theoretical and practical knowledge of major approaches for developing generative models, including variational autoencoders, generative adversarial networks, autoregressive models, energy-based models, and diffusion models.
  • Implement key architectures for tasks such as text, image, and audio generation through hands-on programming assignments.
  • Learn techniques for evaluation, bias detection, controllability, and multimodal generation of generative models.
  • Develop expertise in state-of-the-art generative modeling techniques and apply them to real-world problems across different industries.

Prerequisites

  • Coursework in machine learning, deep learning, and probability.
  • Programming experience in Python and common ML/DL libraries.

Target Audience

  • Graduate students
  • Researchers
  • Industry practitioners looking to gain advanced skills in generative modeling.

Course Duration: 20 hours

Key Features:

  1. Programming assessment at the end of each session to test the participant’s understanding of the concepts
  2. 2 hours of doubt-clearing sessions every week on the corresponding weekend for topics covered in the week
  3. Mini-projects at regular intervals to understand the application of the concepts learned
  4. Capstone project at the end of the course to implement and understand the application of various concepts covered in the course.
  5. After the course completion, you will be provided with free 50 assessment questions for practice.