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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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Signature Learning Experience
Master Generative AI Tools Course
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Earn an industry-recognized credential and get access to mentor-led guidance throughout your learning journey.

4.7/5 Program Rating
1,761 Learners Enrolled
All level Level
20 hrs Total Duration
English,Hindi Language
Globally trusted accreditations Recognitions that back every SkillsBiz graduate
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Program Overview

Experience a modern, outcome-driven curriculum

Built with industry mentors, this program blends immersive live sessions, simulation projects, and on-demand resources so you can master Mandarin Chinese with confidence.

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

Each element is crafted to accelerate fluency, cultural context, and career readiness.

  • 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
10
Guided Hours
20 hrs.
Program Investment
INR 9,999
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Learning Outcomes

Unlock the capabilities that matter in real-world communication

Every module stacks practical language skills with cultural fluency, so you can speak confidently in professional, academic, and social settings.

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.

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Certificate

Certificate You'll Receive

Earn an industry-recognized certificate upon successful completion of this course.

Master Generative AI Tools Course — Course Certificate
Personalised guidance

Book a discovery call with our program specialists

Get tailored advice on learning paths, certification journeys, and industry opportunities before you enroll.

In a focused 20-minute consultation we map your current proficiency, clarify where you want to get to, and suggest the quickest route to fluency with milestones we can help you hit.

  • Understand the skill gaps holding you back and the modules that can close them in the next cohort.
  • Get a personalised cohort recommendation based on availability, trainer profile, and your weekly bandwidth.
  • Discover global project work and certification add-ons that enhance Mandarin credentials on your CV.

Reserve your slot

Share your details and we will connect within 24 hours to set up a personalised walkthrough.

Curriculum Architecture

Structured modules that build fluency session after session

Explore the complete roadmap—from foundational vocabulary to advanced professional expressions—crafted with practical immersion in mind.

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
Upcoming Batches

Pick a schedule that fits your routine

We run multiple live cohorts so you can line up a batch with your goals, weekly bandwidth, and preferred mode of learning.

New cohorts are being planned

Share your interest and we will notify you as soon as the next batch opens.

Learner Voices

Stories from professionals who accelerated with SkillsBiz

Hear how learners leveraged mentor feedback, immersive projects, and certification support to reach their Mandarin goals.

H
Hiba Hasan
I enjoyed learning how generative AI can be used in marketing, content creation, and automation. The course opened up many creative possibilities for me.
M
Mitali Sharma
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.
A
Ankita Dewan
Generative AI became easier to understand through simple explanations and real projects. I can now create AI-generated visuals, text, and workflows confidently.
V
Vivek Chandel
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.
R
Rohan Mathur
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.
A
Aayush Khurana
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.
Frequently Asked Questions

Your questions, answered in one place

Everything you need to know before you commit—from certification timelines to support during the program.

A basic understanding of machine learning and programming is recommended.
The course duration is 20 hours.
Yes, you will receive a certification after completing the course and assessments.
You will need access to a computer with Python installed and relevant libraries.
Yes, you will have lifetime access to all course materials.
Yes, there is a hands-on project where you will create AI-generated art.
The course is fully online and offered through a combination of video lectures and practical assignments.
Yes, there will be live Q&A sessions scheduled throughout the course.
Absolutely, you can complete the course at your convenience as it is self-paced.
You can pay via credit card, debit card, or online payment services.

Global recognitions that power your profile

We are trusted by leading accreditation bodies, ensuring your certificate is respected by employers worldwide.

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Awards & Recognitions

Celebrated for excellence in outcome-driven education

Our pedagogy, learner outcomes, and mentor network have been acknowledged by industry councils and global education forums.

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