📚 Course Description: Deep Learning for Computer Vision — Master Modern AI Vision Techniques

This hands-on course offers a comprehensive journey through the core concepts and cutting-edge techniques in deep learning for computer vision, designed for learners who already have basic knowledge of Python and machine learning.

Starting with the fundamentals of neural networks and CNNs, the course gradually introduces advanced topics like transfer learning, object detection, image segmentation, and model deployment, using powerful tools like PyTorch and Google Colab.

You will explore and implement real-world architectures such as MobileNet V2 for efficient vision tasks, Faster R-CNN and YOLO for object detection, and U-Net and Mask R-CNN for semantic and instance segmentation. Each concept is reinforced through weekly coding projects, helping you apply what you learn in practice.

By the end of the course, you’ll have the skills to:

• Build and train custom CNNs for image classification

• Fine-tune state-of-the-art pretrained models

• Implement end-to-end object detection and segmentation pipelines

• Deploy deep learning models using TorchScript and ONNX

Whether you're a developer, researcher, or aspiring ML engineer, this course equips you with the tools and practical knowledge to build robust computer vision applications in the real world.