Technology & IT

Master Generative AI Tools Course

Unlock your creativity with our comprehensive Generative AI Tools Course. Learn the fundamentals and applications of generative models in just 20 hours!

Foundational theory and models: Clear, approachable lessons on VAEs, GANs, diffusion models, and transformer-based generative architectures. Hands-on practical workflows: Guided labs covering prompt engineering, fine-tuning, data preparation, and building end-to-end generative pipelines using popular tools and libraries. Real-world projects & deployment: Capstone projects, model evaluation, ethical considerations, and best practices for deploying generative systems to production.

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20 hrs

Total duration

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Live, mentor-led classes Recognized certificate Placement support Hands-on projects Lifetime access

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Program overview

About this program

This Technology & IT program blends live, mentor-led sessions with hands-on projects and real-world case studies, so you build genuinely job-ready skills — not just theory. You'll learn from working industry experts, apply what you learn on practical assignments, and finish with a portfolio, a recognized certificate, and dedicated placement support to help you land the right role.

Unlock your creativity with our comprehensive Generative AI Tools Course. Learn the fundamentals and applications of generative models in just 20 hours!

This course is designed for individuals looking to deepen their understanding of generative AI tools. Over 20 hours of immersive content, you will explore the theoretical foundations, practical applications, and advanced techniques in generative modeling. By the end of the course, you will be equipped to create and implement generative AI solutions in various domains.

Why learners love this program

Foundational theory and models: Clear, approachable lessons on VAEs, GANs, diffusion models, and transformer-based generative architectures.
Hands-on practical workflows: Guided labs covering prompt engineering, fine-tuning, data preparation, and building end-to-end generative pipelines using popular tools and libraries.
Real-world projects & deployment: Capstone projects, model evaluation, ethical considerations, and best practices for deploying generative systems to production.

Lectures

Hours

All level

Level

Globally trusted accreditations

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Trusted by leading accreditation bodies — so your certificate is respected by employers worldwide.

Learning outcomes

What you'll be able to do

Explain core concepts of generative modeling, including latent spaces, probability distributions, and common loss functions.

Differentiate major generative architectures (GANs, VAEs, Diffusion models, autoregressive models, and transformer-based generators) and when to use each.

Navigate and use popular tools and frameworks for generative AI (PyTorch/TensorFlow, Hugging Face, Diffusers, and common model APIs).

Prepare and curate datasets for generative tasks, including preprocessing, augmentation, and synthetic data generation strategies.

Build, train, and fine-tune generative models on custom datasets, applying best practices for stability and convergence.

Apply prompt engineering and conditioning techniques for controllable generation across text, image, and audio modalities.

Evaluate generative model performance using quantitative metrics and human-centered evaluation; identify issues like mode collapse and overfitting.

Optimize models for deployment: latency, memory, quantization, batching, and cost-effective inference strategies.

Integrate multimodal workflows (text-to-image, image-to-image, text-to-audio) and combine models into end-to-end pipelines.

Implement safety, bias mitigation, licensing, and copyright-aware practices for responsible generative AI use.

Design and deliver end-to-end projects demonstrating real-world applications (creative content, product design, synthetic data for ML).

Troubleshoot and debug common training and inference problems, and adopt iterative improvement workflows for model development.

Curriculum

Structured modules that build real skills

16 modules · designed for progressive, hands-on learning.

What is Generative AI?

Define generative AI and describe its core capabilities and typical outputs.

0.50 hrs

Core concepts and problem framing

Explain common problem formulations such as generation, translation, and synthesis with examples.

0.50 hrs

Historical milestones

Summarize key breakthroughs that shaped modern generative modeling and their significance.

0.50 hrs

Impact on industries

Describe how generative AI is transforming sectors like art, entertainment, and design.

0.50 hrs

Terminology and common examples

Identify common terms and showcase representative use cases to ground further learning.

0.50 hrs

Neurons, layers, and activation functions

Explain neuron operations, layer types, and activation functions used in neural networks.

0.50 hrs

Forward and backward propagation

Describe how forward passes and backpropagation enable model learning from data.

0.50 hrs

Optimization and loss functions

Understand common loss functions and optimization algorithms used to train models.

0.50 hrs

Architectures relevant to generative models

Identify architectures such as convolutional and transformer layers used in generative systems.

0.50 hrs

Regularization and generalization

Explain techniques to prevent overfitting and improve model generalization.

0.50 hrs

GAN fundamentals

Describe the generator-discriminator framework and adversarial training dynamics.

0.50 hrs

Losses and training stability

Explain common GAN loss formulations and strategies to improve training stability.

0.50 hrs

Popular GAN variants

Compare variants such as DCGAN, WGAN, and StyleGAN and their intended benefits.

0.50 hrs

GANs for image synthesis

Understand how GANs generate realistic images and common pipelines for image tasks.

0.50 hrs

Evaluation metrics for GANs

Identify metrics like FID and IS and discuss their strengths and limitations.

0.50 hrs

VAE formulation and latent spaces

Explain the encoder-decoder structure and how VAEs model latent distributions.

0.50 hrs

Evidence lower bound and training

Describe the ELBO objective and how VAEs are optimized during training.

0.50 hrs

VAEs vs GANs

Compare capabilities, strengths, and limitations of VAEs relative to GANs.

0.50 hrs

Applications of VAEs

Explore practical uses such as data compression, interpolation, and conditional generation.

0.50 hrs

Improving VAE outcomes

Learn techniques to enhance VAE sample quality and latent space utility.

0.50 hrs

Generative art and music

Describe workflows for producing art and music using generative models and tools.

0.50 hrs

Content creation and media

Explain how generative AI supports content generation, augmentation, and personalization.

0.50 hrs

Design, advertising, and product prototyping

Identify how generative models accelerate design iteration and prototyping in industry.

0.50 hrs

Scientific and technical applications

Discuss applications in areas like drug discovery, simulation, and data augmentation.

0.50 hrs

Case studies and success stories

Analyze real-world examples to extract lessons and best practices for adoption.

0.50 hrs

Bias and fairness considerations

Explain how generative models can reproduce biases and approaches to mitigate them.

0.50 hrs

Copyright, ownership, and provenance

Discuss intellectual property concerns and strategies for tracking provenance of generated content.

0.50 hrs

Responsible deployment practices

Identify governance, auditing, and monitoring practices to deploy models responsibly.

0.50 hrs

Security risks and misuse

Understand potential misuse scenarios and defensive measures to reduce harm.

0.50 hrs

Regulatory and societal implications

Discuss emerging regulatory trends and societal impacts of widespread generative AI use.

0.50 hrs

Project scoping and dataset selection

Learn to define project goals, select datasets, and identify success criteria for a generative project.

0.50 hrs

Tool selection and environment setup

Choose appropriate libraries and set up a development environment for building generative models.

0.50 hrs

Building and training models

Implement model training loops and iterate on architecture and hyperparameters to improve results.

0.50 hrs

Evaluation and refinement

Apply evaluation metrics, perform qualitative reviews, and refine models based on feedback.

0.50 hrs

Presentation and documentation

Prepare project deliverables including documentation, demos, and insights from experiments.

0.50 hrs

Hyperparameter tuning and tricks

Learn practical tuning strategies and heuristics to improve model convergence and output quality.

0.50 hrs

Model compression and deployment considerations

Understand techniques for model compression and trade-offs when deploying generative systems.

0.50 hrs

Emerging research directions

Survey upcoming trends and technologies likely to shape the future of generative AI.

0.50 hrs

Ethical and societal outlook

Reflect on long-term ethical considerations and potential societal impacts of generative technology.

0.50 hrs

Final assessment and certification

Complete a capstone assessment to demonstrate mastery and qualify for course certification.

0.50 hrs

Your credential

The certificate you'll earn

Earn an industry-recognized certificate on successful completion of this program.

Master Generative AI Tools Course certificate

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Reviews

What our learners say

I enjoyed learning how generative AI can be used in marketing, content creation, and automation. The course opened up many creative possibilities for me.

HHHiba Hasan

Working with image and text models gave me a complete picture of how AI creation works. I feel confident using generative tools for content and design tasks now.

MSMitali Sharma

Generative AI became easier to understand through simple explanations and real projects. I can now create AI-generated visuals, text, and workflows confidently.

ADAnkita Dewan

This program gave me confidence to build AI-driven content and prototypes. Understanding generative models and their applications made AI feel accessible instead of overwhelming.

VCVivek Chandel

Working with multiple AI platforms made me realize how powerful prompt engineering can be. The hands-on sessions helped me create meaningful outputs with accuracy and consistency.

RMRohan Mathur

Understanding Generative AI finally felt simple after joining this course. Learning how tools like GPT, Midjourney, and stable models work behind the scenes gave me a solid foundation for real-world projects.

AKAayush Khurana

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