Multimodal AI: Master Course — Build AI That Sees, Hears, and Reads
Master multimodal AI: vision-language models, image generation, audio/speech, video understanding, document intelligence, multimodal RAG, and agents. 14 modules, 6 projects, 65+ hours.
Course Snapshot
| Course Code | AIM-609 |
| Duration | 65+ hours |
| Modules | 14 |
| Projects | 6 |
| Phase | 5 — PyTorch & Multimodal AI |
| Skill Level | Intermediate to Advanced |
| Format | Self-paced + live cohorts |
| Price | ₹7,999 (early bird ₹5,499) |
| Last Updated | July 2026 |
Course Overview
The next frontier of AI is not text alone — it is models that understand images, audio, video, and documents alongside language. This course takes you from the foundations of multimodal learning to building production systems that process multiple modalities end-to-end. You will start with vision-language models, understanding how CLIP aligns image and text embeddings and how modern VLMs like LLaVA and Qwen-VL fuse visual features with language generation. You will then dive into image generation with diffusion models, audio and speech AI with Whisper and TTS, video understanding with temporal attention, and document intelligence for OCR and layout analysis. The course covers multimodal RAG — retrieving and generating over images, tables, and mixed-media documents — and multimodal agents that can see screens, hear commands, and take actions. You will work with open-source multimodal models from Hugging Face, fine-tune VLMs on custom image-text datasets, and deploy multimodal pipelines behind production APIs. Fourteen modules, six hands-on projects, and sixty-five hours of deep, practical learning. By the end, you will be able to build AI systems that understand the world the way humans do — through multiple senses simultaneously.
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This course is designed for specific professional profiles. If you match any of these, you will get maximum value.
ML Engineers & AI Developers
Engineers building applications that need to process images, audio, video, or documents alongside text — from visual search to automated document processing.
Product Managers & Tech Leads
Leaders who need to understand the multimodal AI landscape to make informed build-vs-buy decisions and architect multimodal systems.
Data Scientists & Researchers
Practitioners moving from text-only NLP into multimodal learning, needing hands-on experience with VLMs, diffusion models, and audio AI.
Startup Founders
Founders building products in computer vision, audio processing, document automation, or AI agents that require multimodal understanding.
This Course Is NOT For You If:
- Beginners with no Python or ML experience — start with our Prompt Engineering or Hugging Face courses first.
- Those only interested in text-only LLMs — this course focuses on non-text modalities and their integration with language.
- Anyone looking for a purely theoretical course — every module includes hands-on implementation and deployment.
- Learners without access to a GPU — VLM and diffusion model labs require at least a Colab T4 GPU.
What You Will Learn
After completing this course, you will be able to:
- 1 You will be able to explain how CLIP, LLaVA, and modern VLMs align visual and textual representations
- 2 You will be able to build vision-language applications for image captioning, VQA, and visual search
- 3 You will be able to generate and edit images with diffusion models using Stable Diffusion and Flux
- 4 You will be able to build audio AI pipelines with Whisper for speech recognition and TTS for generation
- 5 You will be able to process video with temporal models for action recognition and video question answering
- 6 You will be able to extract structured data from documents with OCR, layout analysis, and table extraction
- 7 You will be able to build multimodal RAG systems that retrieve and generate over images and mixed media
- 8 You will be able to create multimodal agents that see screens, interpret visual content, and take actions
- 9 You will be able to fine-tune vision-language models on custom image-text datasets
- 10 You will be able to deploy multimodal pipelines behind production APIs with proper GPU resource management
Download Free Course Syllabus
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Download Free Syllabus →Complete Course Curriculum
14 modules with detailed lessons. Every lesson includes specific learning points.
MODULE 1 — Multimodal AI Foundations
4 hours- Understand the taxonomy: unimodal vs multimodal vs cross-modal AI systems
- Survey the modality landscape: text, image, audio, video, 3D, and sensor data
- Review the evolution from single-modality models to unified multimodal architectures
- Identify production use cases: visual search, document automation, and content moderation
- Understand shared embedding spaces where different modalities are mapped to a common dimension
- Learn contrastive learning: how CLIP aligns image and text embeddings with InfoNCE loss
- Explore fusion strategies: early fusion, late fusion, and cross-attention fusion
- Compare joint encoding vs cross-modal retrieval for different application types
- Map the full ecosystem: encoders, projectors, LLM backbones, and generation decoders
- Understand the role of vision encoders (ViT, CLIP) and audio encoders (Whisper, AudioLLaMA)
- Survey open-source multimodal models: LLaVA, Qwen-VL, InternVL, and Pixtral
- Set up your environment with Hugging Face transformers, diffusers, and accelerate
MODULE 2 — Vision Language Models
5 hours- Understand CLIP architecture: dual encoders for images and text with contrastive loss
- Use CLIP zero-shot for image classification with natural language prompts
- Build a visual search engine with CLIP embeddings and cosine similarity
- Fine-tune CLIP on a custom domain dataset with contrastive learning
- Understand LLaVA architecture: vision encoder + projector + LLM decoder
- Run LLaVA for image captioning, visual question answering, and multi-image reasoning
- Compare LLaVA with Qwen-VL, InternVL, and Pixtral on benchmark tasks
- Handle high-resolution images with dynamic patching and tiling strategies
- Create an image captioning service with a fine-tuned VLM
- Build a visual question answering chatbot with streaming responses
- Implement visual search and retrieval over a product image catalog
- Deploy a VLM behind a FastAPI server with image upload and text response
MODULE 3 — Image Generation
5 hours- Understand the forward diffusion process: progressively adding noise to images
- Learn the reverse diffusion process: denoising with a neural network to generate images
- Implement a simplified DDPM (Denoising Diffusion Probabilistic Model) in PyTorch
- Understand the role of U-Net architecture in the denoising network
- Generate images with Stable Diffusion using text prompts and negative prompts
- Use control nets for structure-guided generation with edges, depth maps, and poses
- Experiment with Flux for higher quality and better prompt adherence
- Implement image-to-image generation and inpainting with diffusion models
- Build a text-to-image API with Stable Diffusion or Flux behind a FastAPI server
- Implement batch generation with different samplers and scheduler configurations
- Optimise generation speed with xFormers, Flash Attention, and quantized pipelines
- Add safety filters and content moderation to generated images
MODULE 4 — Audio & Speech AI
5 hours- Understand Whisper architecture: encoder-decoder transformer for speech-to-text
- Run Whisper for transcription in 90+ languages with automatic language detection
- Use Whisper for real-time streaming transcription with chunking and VAD
- Compare Whisper variants: tiny, base, small, medium, and large for speed vs accuracy
- Generate natural speech with Bark, Coqui XTTS, and StyleTTS2
- Clone voices with few-shot speaker adaptation techniques
- Control prosody, emotion, and speaking rate in generated speech
- Build a multilingual TTS pipeline with voice preservation across languages
- Classify audio events with AudioLLaMA and audio classification models
- Generate music with MusicGen and AudioCraft
- Build an audio search and retrieval system with audio embeddings
- Process audio in real-time with streaming inference pipelines
MODULE 5 — Video Understanding
5 hours- Understand video as a sequence of frames: temporal dimension adds complexity over images
- Learn 3D convolutions, temporal attention, and video transformers
- Extract frames from video and build frame-level embeddings with CLIP or ViT
- Implement temporal pooling strategies: mean, max, and attention-based aggregation
- Build a video question answering system with a VLM and temporal reasoning
- Generate video captions and summaries with multimodal models
- Handle long videos with sliding window and hierarchical processing
- Evaluate video understanding with action recognition and temporal grounding benchmarks
- Build a video search engine that finds moments by natural language query
- Create an automated video summarisation pipeline with keyframe extraction
- Implement action recognition for surveillance and sports analytics
- Deploy a video analysis API with GPU-accelerated frame processing
MODULE 6 — Document Intelligence
5 hours- Use Tesseract, PaddleOCR, and TrOCR for text extraction from images and scanned documents
- Handle multi-language OCR with script detection and language-specific models
- Improve OCR accuracy with preprocessing: deskewing, denoising, and binarisation
- Build a document digitisation pipeline from scan to structured text
- Detect document layout with LayoutLM and DiT for region classification
- Extract tables with table structure recognition models and convert to structured formats
- Process forms, invoices, and receipts with key-value pair extraction
- Handle complex layouts with multi-column text and mixed content types
- Use VLMs for zero-shot document question answering on scanned documents
- Extract structured data from invoices, contracts, and financial reports
- Build a document classification system for routing and processing workflows
- Handle handwritten text and mixed print-plus-handwritten documents
MODULE 7 — Multimodal RAG
5 hours- Extend RAG to retrieve and generate over images, tables, and mixed-media documents
- Build a multimodal embedding index with CLIP for image-text joint retrieval
- Handle PDF documents with embedded images, charts, and tables in a RAG pipeline
- Implement cross-modal retrieval: text query to image results and vice versa
- Chunk documents by layout regions: text blocks, images, tables, and figures
- Create separate embedding indices for text and image content with unified querying
- Use ColPali and visual document retrieval models for page-level matching
- Build a hybrid retrieval system combining text and visual similarity scores
- Construct a complete multimodal RAG pipeline from ingestion to generation
- Use a VLM as the reader model to answer questions from retrieved visual content
- Implement citation and grounding for multimodal responses with source images
- Evaluate multimodal RAG with faithfulness, relevance, and visual grounding metrics
MODULE 8 — Multimodal Agents
5 hours- Understand the architecture of multimodal agents: perception, reasoning, and action
- Build a screen-reading agent that interprets UI screenshots and takes actions
- Use computer use models for automated web interaction and form filling
- Implement agentic loops with visual feedback for iterative task completion
- Give agents access to OCR, image search, and chart-reading tools
- Build a multimodal function-calling pipeline where the agent selects tools based on visual input
- Implement multi-step reasoning over visual content with chain-of-thought
- Handle ambiguous visual queries with clarification and re-planning
- Build an agent that reads a dashboard screenshot and generates a natural language report
- Create an agent that processes a PDF invoice and extracts structured data with verification
- Build a visual assistant that answers questions about images with tool-augmented reasoning
- Deploy a multimodal agent behind a chat interface with image upload support
MODULE 9 — Open Source Multimodal
4 hours- Survey open-source VLMs: LLaVA, Qwen-VL, InternVL, Pixtral, and Phi-3 Vision
- Compare capabilities, model sizes, licenses, and hardware requirements
- Run open-source VLMs locally with transformers and vLLM
- Benchmark open-source VLMs against proprietary models like GPT-4o and Gemini
- Explore open-source audio models: Whisper, Bark, MusicGen, and AudioCraft
- Run open-source video models: VideoLLaMA, LLaVA-Video, and Qwen-VL for video
- Compare open-source image generation: Stable Diffusion, Flux, and SDXL variants
- Build a fully open-source multimodal pipeline without any proprietary API dependencies
- Evaluate models on quality, speed, memory, and license for your use case
- Understand quantized variants: GGUF, AWQ, and GPTQ for efficient local deployment
- Mix and match open-source models for different modalities in a single pipeline
- Contribute back to open-source multimodal model communities
MODULE 10 — Multimodal Fine-Tuning
5 hours- Prepare image-text instruction datasets for VLM fine-tuning
- Fine-tune LLaVA with LoRA on a custom visual instruction dataset
- Freeze the vision encoder and train only the projector and LLM layers
- Evaluate fine-tuned VLMs on domain-specific visual question answering tasks
- Fine-tune Stable Diffusion with LoRA on a custom image dataset for style transfer
- Train a textual inversion or DreamBooth model for custom concept generation
- Fine-tune ControlNet for domain-specific structure-guided generation
- Evaluate fine-tuned diffusion models with FID, CLIP score, and human evaluation
- Fine-tune Whisper on a domain-specific audio dataset for improved transcription
- Adapt TTS models to new voices with few-shot speaker adaptation
- Fine-tune audio classification models for custom sound event detection
- Handle data augmentation for audio: speed perturbation, noise injection, and SpecAugment
MODULE 11 — Industry Applications
4 hours- Build a medical image analysis pipeline with VLMs for radiology and pathology
- Process medical documents with OCR and multimodal extraction
- Implement HIPAA-compliant on-premise multimodal AI for patient data
- Evaluate medical multimodal AI with domain-specific safety and accuracy metrics
- Build a visual product search engine with CLIP embeddings
- Create automated product description generation from product images
- Implement virtual try-on and visual recommendation systems
- Process user-generated visual content for moderation and tagging
- Automate invoice and receipt processing with document intelligence
- Build a contract analysis system with multimodal document understanding
- Create automated video content moderation and timestamping for media companies
- Implement compliance monitoring with multimodal content analysis
MODULE 12 — Multimodal Evaluation
4 hours- Evaluate VLMs on standard benchmarks: VQAv2, GQA, TextVQA, and MMMU
- Run MMBench and MME for comprehensive multimodal reasoning evaluation
- Understand the difference between perception, reasoning, and knowledge benchmarks
- Compare open-source VLMs on benchmark leaderboards for your use case
- Evaluate image generation with FID, IS, CLIP score, and human preference metrics
- Measure audio quality with WER for speech recognition and MOS for TTS
- Benchmark video understanding with action recognition and temporal grounding metrics
- Design custom evaluation protocols for domain-specific multimodal tasks
- Design A/B testing protocols for multimodal outputs (images, audio, video)
- Conduct blind preference tests for generated visual and audio content
- Measure inter-annotator agreement with Cohen's kappa and Fleiss' kappa
- Build an evaluation dashboard for continuous multimodal quality monitoring
MODULE 13 — Production Deployment
4 hours- Deploy VLMs with vLLM for high-throughput multimodal inference
- Serve diffusion models with optimised pipelines and batch generation
- Build a unified API that handles text, image, audio, and video inputs
- Manage GPU memory across multiple modalities with dynamic allocation
- Implement asynchronous processing for long-running video and audio tasks
- Use message queues for batch processing of multimodal content
- Scale horizontally with multiple GPU workers and load balancing
- Monitor multimodal pipelines with latency tracking per modality
- Reduce inference costs with quantization for vision and audio models
- Cache multimodal embeddings to avoid redundant computation
- Implement fallback strategies when GPU resources are constrained
- Track per-modality costs and optimise the most expensive pipeline stages
MODULE 14 — Capstone Projects
5 hours- Build a VQA system with an open-source VLM and deploy behind an API
- Support image upload and natural language questions with streaming answers
- Evaluate on a held-out set of images and questions
- Add visual grounding to highlight relevant image regions in responses
- Build a RAG system that retrieves and generates over PDFs with images and tables
- Implement multimodal chunking with separate text and image indices
- Use a VLM reader to answer questions from retrieved visual content
- Evaluate with faithfulness and visual grounding metrics
- Build a custom image generation pipeline with fine-tuned Stable Diffusion
- Create an audio transcription and summarisation pipeline with Whisper
- Build a video search and summarisation system with temporal understanding
- Deploy a multimodal agent that processes mixed media and takes actions
Real-World Projects & Portfolio Outcomes
6 portfolio-worthy projects that prove your skills to employers.
Visual Question Answering System
Build a VQA system using an open-source VLM (LLaVA or Qwen-VL) that accepts image uploads and natural language questions, then returns accurate answers with visual grounding. Deploy behind a FastAPI server with streaming responses.
Multimodal RAG for Documents
Build a RAG pipeline that ingests PDFs with embedded images, tables, and charts. Implement multimodal chunking, dual embedding indices, and a VLM reader that answers questions from retrieved visual and textual content.
Custom Image Generation Pipeline
Fine-tune Stable Diffusion with LoRA on a custom image dataset for a specific style or domain. Build a text-to-image API with control net support, batch generation, and content moderation.
Audio Transcription & Summarisation Pipeline
Build an end-to-end pipeline that transcribes audio with Whisper, summarises the transcript with an LLM, and generates a spoken summary with TTS. Support multi-language input and output.
Video Search & Summarisation
Build a system that processes video files, extracts keyframes, creates a searchable index with CLIP embeddings, and generates natural language summaries of video content. Support text-based moment retrieval.
Multimodal Agent for Document Processing
Build a multimodal agent that processes invoices, contracts, or forms — using OCR, VLM understanding, and tool-augmented reasoning to extract structured data with verification and human-in-the-loop review.
Tools, Technologies & Models Covered
LLM Models Covered
| LLaVA-1.6 | LLaVA Project | VQA, image captioning |
|---|---|---|
| Qwen-VL 2 | Alibaba | VQA, document understanding |
| InternVL 2 | Shanghai AI Lab | High-res VQA, OCR |
| CLIP | OpenAI | Visual search, embedding |
| Stable Diffusion XL | Stability AI | Image generation |
| Flux | Black Forest Labs | High-quality image generation |
| Whisper | OpenAI | Speech recognition |
| Bark | Suno | Text-to-speech |
| MusicGen | Meta | Music generation |
| VideoLLaMA | DAMO Academy | Video understanding |
Tools & Frameworks Covered
| Hugging Face Transformers | VLM, audio, and video model loading | Deep |
|---|---|---|
| Hugging Face Diffusers | Diffusion model inference and fine-tuning | Deep |
| Hugging Face PEFT | LoRA fine-tuning for VLMs | Intermediate |
| vLLM | High-throughput multimodal inference | Intermediate |
| PaddleOCR | OCR and text extraction | Intermediate |
| LayoutLM | Document layout analysis | Intermediate |
| FastAPI | API deployment for multimodal pipelines | Intermediate |
| Librosa | Audio processing and feature extraction | Intermediate |
| OpenCV | Image and video processing | Intermediate |
Book a Free Counselling Call
Not sure which course is right for you? Book a free 1:1 counselling call with our AI training advisors.
Book Free Counselling Call →Your Instructor
Dr. Kavya Reddy
Lead Multimodal AI Researcher
9+ years in computer vision and multimodal learning
Built multimodal systems for healthcare imaging, e-commerce search, and media analysis
2,500+ engineers trained
Multimodal AI is not just bolting a vision encoder onto an LLM. It is about building systems that genuinely understand the rich, multi-sensory world we live in — and that requires deep architectural understanding.
Course Team & Curriculum Design
Our teaching team includes researchers from top AI labs and engineers who have deployed multimodal systems in healthcare, retail, and media. Teaching assistants provide code review and debugging support during live cohorts.
Learning Path & Prerequisites
Prerequisites
- Intermediate Python and basic PyTorch or TensorFlow
- Familiarity with Hugging Face transformers (load model, tokenizers, generate)
- Basic understanding of transformer architecture and attention
- Familiarity with CNNs and image processing concepts (convolution, pooling)
- Access to a GPU with at least 16GB VRAM (Colab, Kaggle, or local)
- Completion of AIM-603 (Hugging Face) and AIM-606 (LLMs Pro) recommended
Recommended Learning Paths
ML Engineer building multimodal applications
- Complete Modules 1–2 for multimodal foundations and VLMs
- Take Modules 3–5 for image, audio, and video generation and understanding
- Focus on Module 7 for multimodal RAG and Module 13 for deployment
- Build the VQA and multimodal RAG capstone projects
Product builder in a specific industry
- Take Modules 1–2 and Module 6 for document intelligence
- Focus on Module 11 for your industry (healthcare, e-commerce, or finance)
- Complete Module 13 for production deployment
- Build the document processing agent project
Researcher exploring multimodal learning
- Deep-dive into Modules 1–2 for representation learning theory
- Focus on Module 10 for multimodal fine-tuning techniques
- Take Module 12 for rigorous evaluation methodology
- Build the custom image generation or VLM fine-tuning project
What Comes After This Course
Pricing & Enrollment
Choose the plan that fits your learning goals. All plans include a 7-day money-back guarantee.
| Self-Paced | ₹7,999 | All 14 modules, 6 projects, community Discord, lifetime access |
|---|---|---|
| Early Bird | ₹5,499 | Same as Self-Paced — limited time before cohort launch |
| Cohort Live | ₹14,999 | Live sessions, TA support, code review, and certificate |
What Is Included
- 14 modules with 45+ hands-on lab notebooks
- 6 capstone projects with code review
- Pre-configured Colab and Kaggle notebooks for every lab
- Sample datasets for VLM fine-tuning, image generation, and audio processing
- Private Discord community with instructors and TAs
- Lifetime access including future updates
- Certificate of completion with project portfolio
30-day money-back guarantee. Complete the first 4 modules and if the course is not the right fit, get a full refund — no questions asked.
Frequently Asked Questions
Do I need to know computer vision before taking this course?
Basic familiarity with CNNs and image processing helps, but is not required. Module 1 covers the foundations of multimodal representation learning, and Module 2 introduces vision encoders from scratch. If you have used Hugging Face transformers for text models, you can pick up the vision components quickly.
What GPU do I need for the labs?
A Google Colab free T4 (16GB) is sufficient for most labs, including VLM inference, Stable Diffusion generation, and Whisper transcription. Fine-tuning labs benefit from more VRAM — a Colab Pro A100 or local GPU with 24GB+ is recommended for Module 10. We provide size-appropriate models for every GPU tier.
How is this different from the LLMs Pro course?
The LLMs Pro course (AIM-606) focuses on text-only language models. This course (AIM-609) covers models that process images, audio, video, and documents alongside text. If you want to build systems that understand visual content, process audio, or extract data from documents, this is the course for you.
Will I learn to fine-tune vision-language models?
Yes. Module 10 covers fine-tuning LLaVA with LoRA on custom image-text instruction datasets, fine-tuning Stable Diffusion with LoRA and DreamBooth, and adapting Whisper for domain-specific audio. You will gain hands-on experience with all three modality fine-tuning workflows.
Can I use proprietary models like GPT-4o or Gemini in the projects?
The course focuses on open-source models so you can deploy everything locally without API costs. However, we discuss how to integrate proprietary multimodal APIs (GPT-4o, Gemini) as alternatives, and some projects allow you to choose between open-source and proprietary models.
Are the projects suitable for a portfolio?
Absolutely. Each of the 6 capstone projects produces a deployable system — a VQA API, a multimodal RAG pipeline, a custom image generator, an audio processing pipeline, a video search engine, and a multimodal agent. These are substantial projects that demonstrate real multimodal AI engineering skills to employers.
Will I get a certificate?
Yes. Complete all 6 capstone projects and submit them for code review. Upon passing, you receive a certificate with links to your project portfolio, suitable for sharing on LinkedIn and with employers.
What to Learn Next
Continue your AI learning journey with these recommended courses.
Fine-Tuning LLMs: Advanced Batch
LoRA, QLoRA, DPO, and GRPO for training custom models
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