Generative AI

About The Course

About Generative AI Course

The Generative AI training at Ashok IT is designed to equip learners with skills to build, fine-tune, and deploy AI models for text, images, and code. This program covers the fundamentals of AI, machine learning, deep learning, NLP, and large language models (LLMs), along with hands-on practice in Generative AI frameworks such as GPT, Diffusion, and GANs. With real-time projects, learners gain the ability to design and implement Generative AI solutions for real-world applications.

Generative AI Course Description

Our Generative AI course provides a structured learning path starting with Python programming, statistics, and machine learning before diving into advanced deep learning, NLP, and transformers. You will explore Generative AI concepts, LLMs, prompt engineering, LangChain, and fine-tuning techniques. The course also covers deployment and scaling of Generative AI models using FastAPI, Docker, and cloud platforms.

By the end of the training, learners will have practical experience in building chatbots, summarization tools, Q&A systems, and image generation apps, making them ready for Generative AI roles in the industry.

Generative AI Course Objectives

The objectives of the Generative AI training are:

• To introduce the concepts of Generative AI, GPT, Diffusion Models, and GANs.
• To teach Python, Numpy, Pandas, and data visualization for AI workflows.
• To provide foundations in statistics, probability, and linear algebra for AI.
• To train learners in machine learning and deep learning techniques.
• To build expertise in NLP pipelines, embeddings, and transformer models.
• To teach prompt engineering and LangChain for building advanced AI apps.
• To cover fine-tuning, RAG (Retrieval Augmented Generation), and deployment.
• To prepare learners for real-time Generative AI projects and interviews.

Key Highlights:

• Foundations of AI and Machine Learning: Understand the key principles behind AI, including supervised, unsupervised, and reinforcement learning.
• Generative Models: Master cutting-edge generative models like GANs, VAEs, and diffusion models.
• Natural Language Processing (NLP): Learn how large language models like GPT generate human-like text and how to fine-tune them for various tasks.
• AI for Image Generation: Explore AI techniques for creating images using GANs and other generative methods.
• Ethics and AI: Gain insights into the ethical considerations surrounding generative AI, including bias, ownership, and responsible AI practices.
• Real-Time Projects: Work on real-world projects such as generating text, images, and music, using advanced generative AI techniques.