What qualifications do I need to specialize in Generative AI?

A background in computer science or engineering helps, but practical AI skills and hands-on training are key.

Is this course really 100% online? Do I need to attend any classes in person?

This course is completely online, so there’s no need to show up to a classroom in person. You can access your lectures, readings and assignments anytime and anywhere via the web or your mobile device.

Can I just enroll in a single course?

Yes! To get started, click the course card that interests you and enroll. You can enroll and complete the course to earn a shareable certificate. When you subscribe to a course that is part of a Specialization, you’re automatically subscribed to the full Specialization. Visit your learner dashboard to track your progress.

Is financial aid available?

Yes. In select learning programs, you can apply for financial aid or a scholarship if you can’t afford the enrollment fee. If fin aid or scholarship is available for your learning program selection, you’ll find a link to apply on the description page.

Can I take the course for free?

No, you cannot take this course for free. When you enroll in the course, you get access to all of the courses in the Specialization, and you earn a certificate when you complete the work. If you cannot afford the fee, you can apply for financial aid.

Will I earn university credit for completing the Specialization?

This Specialization doesn't carry university credit, but some universities may choose to accept Specialization Certificates for credit. Check with your institution to learn more.

Generative AI Models and Transformer Networks Certification Specialization

Generative AI Models and Transformer Networks Certification Specialization

Build VAEs GANs and Transformers with AI

Instructor: Priyanka Mehta

Included with Coursera Plus

Learn more

3 course series

Get in-depth knowledge of a subject

3.3

(5 reviews)

Beginner level

Recommended experience

1 month at 10 hours a week

Flexible schedule

Earn a career credential

Share your expertise with employers

3 course series

Get in-depth knowledge of a subject

3.3

(5 reviews)

Beginner level

Recommended experience

1 month at 10 hours a week

Flexible schedule

Earn a career credential

Share your expertise with employers

What you'll learn

Build VAEs, GANs, and Autoencoders to generate realistic synthetic data
Apply attention mechanisms and Transformer models like GPT and BERT
Use Retrieval Augmented Generation (RAG) to enhance GenAI output accuracy
Deploy GenAI solutions to solve real-world business problems

Overview

This specialization provides a hands-on pathway to mastering Generative AI techniques from foundational architectures to cutting-edge deployment strategies. Learn to build and train Autoencoders, VAEs, and GANs using TensorFlow to generate synthetic data and realistic outputs. Dive into attention mechanisms and Transformer models powering GPT and BERT. Apply RAG for improved accuracy and analyze emerging GenAI trends to create industry-ready solutions.

By the end of this program, you will be able to:

- Train Generative Models: Build and evaluate VAEs and GANs using real-world data

- Generate Synthetic Data: Use VAEs and GANs to create images and other outputs

- Apply Transformer Models: Leverage attention mechanisms in models like GPT and BERT

- Improve Output Accuracy: Use Retrieval Augmented Generation (RAG) for enhanced results

- Deploy GenAI Solutions: Translate emerging model trends into industry-ready applications

Ideal for developers, ML engineers, and AI enthusiasts exploring next-gen model development.

What’s included

Shareable certificate

Add to your LinkedIn profile

Taught in English

Advance your subject-matter expertise

Learn in-demand skills from university and industry experts
Master a subject or tool with hands-on projects
Develop a deep understanding of key concepts
Earn a career certificate from Simplilearn

Specialization - 3 course series

Project Overview 1: Sentiment Classification on Product Reviews

Analyze customer reviews to classify sentiment as positive or negative. Learn to clean data, extract features, and build a sentiment model using machine learning in Python. Gain practical skills in NLP for e-commerce and feedback analysis.

Project Overview 2: Generating Fake Images with GANs

Build and train a Generative Adversarial Network (GAN) to create realistic images. Work on generator and discriminator models, training loops, and evaluation using TensorFlow or PyTorch. Learn key concepts in adversarial learning and computer vision.

Foundations of Generative AI Models

Course 13 hoursView course

What you'll learn

Train and evaluate generative AI models using real-world techniques
Apply Retrieval Augmented Generation (RAG) to improve output accuracy
Understand emerging trends in GenAI architecture and deployment
Translate GenAI advancements into practical, industry-ready solutions

Skills you'll gain

Generative Model Architectures, Generative AI, Natural Language Processing, Responsible AI, Scalability, OpenAI, Real Time Data, Deep Learning, Large Language Modeling, Prompt Engineering, and ChatGPT

Introduction Course to Autoencoders, VAEs, and GANs

Course 23 hoursView course

What you'll learn

Build and train Autoencoders and VAEs using TensorFlow
Use VAEs for generating synthetic data like images
Understand and apply GAN architecture and training techniques
Create realistic outputs with GANs for real-world use cases

Skills you'll gain

Generative Model Architectures, Generative AI, Unsupervised Learning, Tensorflow, Deep Learning, Applied Machine Learning, Artificial Neural Networks, and Image Quality

Attention Mechanisms and Transformer Models Course

Course 34 hoursView course

What you'll learn

Apply self-attention and multi-head attention in deep learning models
Understand transformer architecture and its key components
Explore the role of attention in powering models like GPT and BERT
Analyze real-world GenAI applications in NLP and image generation

Skills you'll gain

Large Language Modeling, Generative Model Architectures, Natural Language Processing, Deep Learning, OpenAI, Generative AI, Prompt Engineering, Artificial Neural Networks, and Machine Learning Methods