Natural Language Processing (NLP)
NLP covers the full arc of language processing: from tokenisation and classical statistical methods through word embeddings to transformer-based models.
2 months Classroom & Online
View syllabus 6 modules
AI & Advanced Technologies
Generative AI
Text, image, audio — generation across every modality.
Generative AI covers creation across modalities: text, images, audio and video. You learn the model families behind each — transformers, diffusion models, GANs, VAEs — and then build with them.
2 months Classroom & Online Intermediate 6 modules
Generative AI Text, image, audio — generation across every modality.
Overview
What this course is, plainly
Generative AI covers creation across modalities: text, images, audio and video. You learn the model families behind each — transformers, diffusion models, GANs, VAEs — and then build with them.
The emphasis is on shipping. You integrate generation into real applications, control output with conditioning and structured prompts, manage cost, and handle the licensing, provenance and consent questions that come attached to generative work.
Why choose this course
- All four modalities: text, image, audio, video
- Diffusion models and transformers explained properly
- ControlNet, LoRA and conditioning techniques
- Production integration, cost and latency
- Licensing, provenance and responsible use
Curriculum
Module-by-module syllabus
The same structure on every Xypron course page, so you can compare tracks like for like. Each module ends in working code, not a quiz.
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Module 1: Generative Model Families
The four architectures behind everything generative.
- Autoencoders & variational autoencoders
- Generative adversarial networks
- Diffusion model fundamentals
- Autoregressive transformers
- Choosing a family for a given problem
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Module 2: Text Generation
Generation where the output is language.
- LLM generation & decoding strategies
- Prompt design for creative and structured output
- Structured output & schema enforcement
- Style, tone & persona control
- Evaluating generated text
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Module 3: Image Generation
The diffusion stack, hands on.
- Stable Diffusion architecture & pipeline
- Prompting, negative prompts & CFG scale
- Image-to-image & inpainting
- ControlNet for structural conditioning
- LoRA fine-tuning for a custom style or subject
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Module 4: Audio & Video Generation
The fastest-moving modalities.
- Text-to-speech & voice synthesis
- Speech-to-text with Whisper
- Music & audio generation models
- Text-to-video model landscape
- Practical limits and current quality ceilings
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Module 5: Building Generative Products
Wrapping a model in something people can use.
- API integration & async job handling
- Streaming and progressive rendering
- Caching, queuing & cost control
- GPU inference & serving options
- UX patterns for non-deterministic output
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Module 6: Ethics, Licensing & Capstone
The part that keeps a generative product out of trouble.
- Training data provenance & copyright
- Model and output licensing
- Watermarking & content provenance standards
- Deepfakes, consent & misuse prevention
- End-to-end capstone project
Projects you'll build
- Branded image generation pipeline with a LoRA-tuned style
- Multimodal content assistant combining text and image
- Voice-enabled generative application with Whisper and TTS
Roles this prepares you for
- Generative AI Engineer
- AI Product Engineer
- Applied AI Developer
- Creative Technologist
Tools you'll master
Python Hugging Face Diffusers Stable Diffusion PyTorch Whisper ComfyUI OpenAI & Anthropic APIs Outcomes
What happens after the last module
The course does not end at the capstone. Placement support runs from your first mock interview until you have an offer — and it is support, not a guarantee.
Resume rewrite
Rebuilt around the projects you actually shipped here, targeted at the roles you are applying for.
Mock interviews
Technical and HR rounds with written feedback after each one, repeated until the gaps close.
Profile sharing
Your profile goes to our hiring partners with a note from the trainer who taught you.
Interview scheduling
We book the interviews and follow through until there is a decision either way.
Next step
Ask us anything about Generative AI
Batch timings, the fee, whether your background fits, what the trainer's day job is — all fair questions. A counsellor will call you back within two business hours.
Python fluency. No prior generative modelling experience required.
The course runs for 2 months in classroom & online mode. Weekday, weekend and evening batches are scheduled continuously — call +91 73493 01293 for the next available start date.
Branded image generation pipeline with a LoRA-tuned style; Multimodal content assistant combining text and image; Voice-enabled generative application with Whisper and TTS — plus an end-to-end capstone project you deploy yourself and can walk an interviewer through line by line.
Yes. Sit a full session with the trainer who would actually teach your batch before you decide anything. Ask for one on WhatsApp or through the enquiry form on this page.
Enquiry
Enquire about Generative AI
Choose WhatsApp or email below. We'll open it with your details already filled in for Generative AI — you just hit send.
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Also in AI & Advanced Technologies
Compare with the neighbouring tracks
Large Language Models (LLM)
This track treats large language models as engineering material rather than magic. You learn how they are trained, why they fail the way they do, and how to build reliable products on top of something fundamentally probabilistic.
2 months Classroom & Online
View syllabus 6 modules
Retrieval Augmented Generation (RAG)
RAG is the pattern behind almost every enterprise AI deployment: retrieve the right context from your own data, then let the model answer from it. It is also the pattern most teams implement badly, and retrieval quality is almost always the reason.
6 weeks Classroom & Online
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Next step
Ready to start Generative AI?
Sit a free demo class before you commit. Ask about batch timings, the trainer, and exactly what the placement support covers.