Modern AI Models for Vision and Multimodal Understanding
Completed by Yerubandi Chetan
January 23, 2026
12 hours (approximately)
Yerubandi Chetan's account is verified. Coursera certifies their successful completion of Modern AI Models for Vision and Multimodal Understanding
What you will learn
Apply Nonlinear Support Vector Machines (NSVMs) and Fourier transforms to analyze and process visual data.
Use probabilistic reasoning and implement Recurrent Neural Networks (RNNs) to model temporal sequences and contextual dependencies in visual data.
Explain the principles of transformer architectures and how Vision Transformers (ViT) perform image classification and visual understanding tasks.
Implement CLIP for multimodal learning, and utilize diffusion models to generate high-fidelity images.
Skills you will gain
- Category: Vision Transformer (ViT)
- Category: Classification Algorithms
- Category: Embeddings
- Category: Recurrent Neural Networks (RNNs)
- Category: Supervised Learning
- Category: Transfer Learning
- Category: Generative Model Architectures
- Category: Machine Learning Methods
- Category: Digital Signal Processing
