📚 Syllabus & Course Curriculum
Data Science & Analytics
Module-by-module breakdown of Computer Vision and Image Processing, from foundations to a certified capstone project.
Outline
Develop a comprehensive understanding of visual computing concepts, including image formation and representation • Analyze the fundamental principles of computer vision, including image processing, feature extraction, and object recognition • Configure visual computing environments using Python and OpenCV to implement basic image processing techniques
Outline
Implement image filtering and enhancement techniques using spatial and frequency domain methods • Design and evaluate image augmentation strategies to improve model robustness and generalization • Extract and analyze visual features from images using techniques such as edge detection, thresholding, and feature descriptors
Outline
Design and implement convolutional neural network (CNN) architectures for image classification and object detection tasks • Evaluate the performance of pre-trained CNN models using transfer learning and fine-tuning techniques • Develop and train custom CNN models using TensorFlow and Keras to solve computer vision problems
Outline
Implement object detection algorithms such as YOLO, SSD, and Faster R-CNN using deep learning frameworks • Analyze and evaluate the performance of semantic segmentation models using metrics such as IoU and accuracy • Develop and train models for instance segmentation and object localization using techniques such as Mask R-CNN and RetinaNet
Outline
Develop and implement video analysis pipelines using techniques such as object tracking and motion estimation • Design and evaluate temporal models for video classification and action recognition tasks • Configure and optimize real-time video processing systems using GPU acceleration and parallel processing techniques
Outline
Optimize and prune deep learning models for computer vision tasks using techniques such as knowledge distillation and quantization • Evaluate the performance of optimized models on edge devices such as Raspberry Pi and NVIDIA Jetson • Deploy and test computer vision models on edge devices using frameworks such as TensorFlow Lite and OpenVINO
Outline
Analyze and evaluate the applications of computer vision in industries such as healthcare, finance, and retail • Develop and implement computer vision solutions for real-world problems such as image classification, object detection, and segmentation • Design and propose computer vision systems for emerging applications such as autonomous vehicles and smart cities
Earn government-registered certification in Computer Vision and Image Processing
e-Certificate and e-Marksheet issued on successful completion.
