Professional Generative AI Engineer Program

By Gangula Vishwas Categories: Professional Programs

About Course

Master Generative AI, LLMs, and modern AI tools to build real-world intelligent applications.

The Generative AI Engineer Professional Program is designed to equip learners with the skills required to build and work with modern generative AI systems, including large language models, diffusion models, and multimodal AI applications. This program focuses on both foundational understanding and practical implementation, enabling learners to develop real-world generative AI solutions.

Learners will gain hands-on experience with neural networks, generative architectures, and transformer-based models, while leveraging AI tools to accelerate development, experimentation, and deployment. The program also emphasizes data preparation, bias mitigation, and responsible AI practices, ensuring learners build reliable and ethical AI systems.

By the end of this program, learners will be capable of building generative AI applications, working with modern frameworks, and preparing for high-demand roles in AI engineering.

Understand core concepts of Generative AI and its applications
Build and work with GANs, VAEs, and diffusion models
Understand transformers and large language models (LLMs)
Develop AI applications using modern frameworks and tools
Work with multimodal AI systems combining text and images
Prepare and clean datasets for generative AI models
Apply bias mitigation and responsible AI practices
Use AI tools to enhance coding, debugging, and experimentation
Build real-world generative AI mini projects
Prepare for AI engineering roles with strong portfolio and skills

Course Content

Module 1: Introduction to Generative AI & Foundations
This module introduces learners to the core concepts of generative AI, including how models generate new data and their applications across industries. It builds a strong conceptual foundation and provides an overview of the generative AI ecosystem, preparing learners for advanced topics.

Module 2: Mathematics for Generative AI
This module covers essential mathematical concepts such as linear algebra, calculus, probability, and statistics required for understanding generative models. The focus is on intuition and application, helping learners connect mathematical concepts with AI implementations.

Module 3: Programming with Python for Generative AI
This module focuses on Python programming tailored for generative AI development. Learners will work with data structures, libraries, and coding practices required for building AI models. AI tools are used to assist in coding and debugging.

Module 4: Neural Networks & Deep Learning Basics
This module introduces neural networks and deep learning concepts that form the backbone of generative AI systems. Learners will understand how models are structured, trained, and optimized.

Module 5: Core Generative Model Architectures
This module explores generative models such as GANs and VAEs, focusing on how they generate new data. Learners will understand model architecture, training processes, and real-world applications.

Module 6: Transformers & Large Language Models Fundamentals
This module introduces transformer architectures and large language models, which power modern generative AI systems. Learners will understand how these models process and generate text and how they are used in real-world applications.

Module 7: Diffusion Models & Multimodal AI Fundamentals
This module focuses on diffusion models and multimodal AI systems that combine text, images, and other data types. Learners will understand how these models generate high-quality outputs and how they are applied in advanced AI applications.

Module 8: Data Collection, Cleaning & Bias Mitigation
This module focuses on preparing datasets for generative AI models. Learners will understand how to collect, clean, and preprocess data while addressing bias and ensuring ethical AI practices.

Module 9: Working with AI/ML Frameworks
This module introduces frameworks such as PyTorch and Hugging Face Transformers. Learners will understand how to build, train, and deploy generative AI models using industry-standard tools.

Module 10: Mini Project – Build a Generative AI Model
This module enables learners to apply their knowledge by building a generative AI project. Learners will work on real-world use cases and develop portfolio-ready applications, demonstrating their ability to implement generative AI systems.

Module 11: Job Readiness & Career Acceleration
This module prepares learners for AI engineering roles by focusing on resume building, portfolio development, LinkedIn optimization, and interview preparation. Learners will also understand industry workflows and expectations. AI tools are used to enhance resumes, simulate interviews, and improve overall career readiness.

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A course by

Gangula Vishwas

Data analyst/ML Engineer

Professional Generative AI Engineer Program

About Course

Master Generative AI, LLMs, and modern AI tools to build real-world intelligent applications.

What Will You Learn?

Course Content

Module 3: Programming with Python for Generative AI
This module focuses on Python programming tailored for generative AI development. Learners will work with data structures, libraries, and coding practices required for building AI models. AI tools are used to assist in coding and debugging.

Module 4: Neural Networks & Deep Learning Basics
This module introduces neural networks and deep learning concepts that form the backbone of generative AI systems. Learners will understand how models are structured, trained, and optimized.

Module 5: Core Generative Model Architectures
This module explores generative models such as GANs and VAEs, focusing on how they generate new data. Learners will understand model architecture, training processes, and real-world applications.

Module 6: Transformers & Large Language Models Fundamentals
This module introduces transformer architectures and large language models, which power modern generative AI systems. Learners will understand how these models process and generate text and how they are used in real-world applications.

Module 7: Diffusion Models & Multimodal AI Fundamentals
This module focuses on diffusion models and multimodal AI systems that combine text, images, and other data types. Learners will understand how these models generate high-quality outputs and how they are applied in advanced AI applications.

Module 8: Data Collection, Cleaning & Bias Mitigation
This module focuses on preparing datasets for generative AI models. Learners will understand how to collect, clean, and preprocess data while addressing bias and ensuring ethical AI practices.

Module 9: Working with AI/ML Frameworks
This module introduces frameworks such as PyTorch and Hugging Face Transformers. Learners will understand how to build, train, and deploy generative AI models using industry-standard tools.

Module 10: Mini Project – Build a Generative AI Model
This module enables learners to apply their knowledge by building a generative AI project. Learners will work on real-world use cases and develop portfolio-ready applications, demonstrating their ability to implement generative AI systems.

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About Course

Master Generative AI, LLMs, and modern AI tools to build real-world intelligent applications.

What Will You Learn?

Course Content

Module 3: Programming with Python for Generative AI This module focuses on Python programming tailored for generative AI development. Learners will work with data structures, libraries, and coding practices required for building AI models. AI tools are used to assist in coding and debugging.

Module 4: Neural Networks & Deep Learning Basics This module introduces neural networks and deep learning concepts that form the backbone of generative AI systems. Learners will understand how models are structured, trained, and optimized.

Module 5: Core Generative Model Architectures This module explores generative models such as GANs and VAEs, focusing on how they generate new data. Learners will understand model architecture, training processes, and real-world applications.

Module 6: Transformers & Large Language Models Fundamentals This module introduces transformer architectures and large language models, which power modern generative AI systems. Learners will understand how these models process and generate text and how they are used in real-world applications.

Module 7: Diffusion Models & Multimodal AI Fundamentals This module focuses on diffusion models and multimodal AI systems that combine text, images, and other data types. Learners will understand how these models generate high-quality outputs and how they are applied in advanced AI applications.

Module 8: Data Collection, Cleaning & Bias Mitigation This module focuses on preparing datasets for generative AI models. Learners will understand how to collect, clean, and preprocess data while addressing bias and ensuring ethical AI practices.

Module 9: Working with AI/ML Frameworks This module introduces frameworks such as PyTorch and Hugging Face Transformers. Learners will understand how to build, train, and deploy generative AI models using industry-standard tools.

Module 10: Mini Project – Build a Generative AI Model This module enables learners to apply their knowledge by building a generative AI project. Learners will work on real-world use cases and develop portfolio-ready applications, demonstrating their ability to implement generative AI systems.

Earn a certificate

Student Ratings & Reviews

About Learning

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Popular Subjects

Get In Touch

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Module 3: Programming with Python for Generative AI
This module focuses on Python programming tailored for generative AI development. Learners will work with data structures, libraries, and coding practices required for building AI models. AI tools are used to assist in coding and debugging.

Module 4: Neural Networks & Deep Learning Basics
This module introduces neural networks and deep learning concepts that form the backbone of generative AI systems. Learners will understand how models are structured, trained, and optimized.

Module 5: Core Generative Model Architectures
This module explores generative models such as GANs and VAEs, focusing on how they generate new data. Learners will understand model architecture, training processes, and real-world applications.

Module 6: Transformers & Large Language Models Fundamentals
This module introduces transformer architectures and large language models, which power modern generative AI systems. Learners will understand how these models process and generate text and how they are used in real-world applications.

Module 7: Diffusion Models & Multimodal AI Fundamentals
This module focuses on diffusion models and multimodal AI systems that combine text, images, and other data types. Learners will understand how these models generate high-quality outputs and how they are applied in advanced AI applications.

Module 8: Data Collection, Cleaning & Bias Mitigation
This module focuses on preparing datasets for generative AI models. Learners will understand how to collect, clean, and preprocess data while addressing bias and ensuring ethical AI practices.

Module 9: Working with AI/ML Frameworks
This module introduces frameworks such as PyTorch and Hugging Face Transformers. Learners will understand how to build, train, and deploy generative AI models using industry-standard tools.

Module 10: Mini Project – Build a Generative AI Model
This module enables learners to apply their knowledge by building a generative AI project. Learners will work on real-world use cases and develop portfolio-ready applications, demonstrating their ability to implement generative AI systems.