About this Course
Adapt multimodal models like LLaVA and PaliGemma for domain-specific visual understanding, document parsing, and image captioning. This AI/ML & Computer Vision curriculum is designed to give you hands-on experience and deep conceptual understanding.
Across 4 intensive modules, you'll tackle real-world challenges and build practical projects that reinforce your learning. By the end of this journey, you'll have the skills and proof of work to demonstrate your expertise.
What you'll learn
Master the core concepts of vision-language model architectures.
Gain hands-on experience with dataset preparation & augmentation for vlm.
Understand the architecture behind parameter-efficient fine-tuning (peft/lora).
Implement production-grade evaluation & deployment of custom vlms.
W1
Vision-Language Model Architectures
Master the core concepts of vision-language model architectures.
4 videos•51m
3 readings
4 topics
1 homework
W2
Dataset Preparation & Augmentation for VLM
Gain hands-on experience with dataset preparation & augmentation for vlm.
4 videos•76m
3 readings
4 topics
1 homework
W3
Parameter-Efficient Fine-Tuning (PEFT/LoRA)
Understand the architecture behind parameter-efficient fine-tuning (peft/lora).
4 videos•124m
3 readings
4 topics
1 homework
W4
Evaluation & Deployment of Custom VLMs
Implement production-grade evaluation & deployment of custom vlms.
4 videos•98m
3 readings
4 topics
1 homework
01
Learn
Watch curated videos and read study resources
02
Practice
Practice what you learned
03
Build Projects
Build projects using your new gained knowledge
04
Submit & Verify
Submit your project and get verified by our system
References
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