Foundational Courses
Computer Vision Specialist
To equip participants with advanced skills in image processing
5
11 enrolled students
Objective
To equip participants with advanced skills in image processing, feature extraction, and deep learning techniques, enabling them to design, develop, and deploy computer vision applications across various industries
Basic To Advance
You will progress through this course from basics to advanced level.
Duration
3 Months
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Modules
Module 1: Data Cleaning, Exploration, and Visualization (4 hrs)
Objective:
Learn techniques for preparing, augmenting, and visualizing image datasets to improve model performance.
Topics:
- Data Cleaning and Preprocessing: Handling corrupted images, resizing, normalization, and addressing class imbalance
- Data Augmentation: Rotation, flipping, cropping, brightness adjustment, and noise addition
- Dataset Exploration: Analyzing class distribution, basic statistics, and visualizing pixel distributions
- Visualization Techniques: Displaying and annotating images, grid views, and overlaying bounding boxes/masks
- Feature Visualization: Extracting and visualizing gradients, edges, heatmaps, and saliency maps
Module 2: OpenCV Basics
Objective:
Develop a solid understanding of OpenCV for fundamental image processing tasks. Learn to read, write, display, and transform images, which will serve as the foundation for more advanced image manipulation and computer vision tasks.
Topics Covered:
- Reading, writing, and displaying images
- Image transformations (resizing, cropping, rotations)
- Edge detection (Canny edge detection)
- Color space conversions (BGR to RGB, grayscale)
Module 3: Introduction to CNNs
Objective:
Learn the fundamentals of convolutional neural networks, including convolutional and pooling layers, to understand how CNNs process visual data. Build on basic image processing knowledge from OpenCV to understand CNNs’ role in complex vision tasks.
Topics Covered:
- Convolutional layers
- Pooling layers
- CNN architectures
Practical:
Build a CNN for image classification on CIFAR-10.
Module 4: Advanced CNN Architectures and Transfer Learning
Objective:
Deepen knowledge of CNN architectures by exploring advanced models like ResNet and VGG. Understand the concept of transfer learning and learn how to fine-tune pre-trained models on custom image datasets to boost performance and reduce training time.
Topics Covered:
- ResNet, VGG, and Transfer Learning
- Practical: Fine-tune a pre-trained model like ResNet on custom image
- classification tasks
Module 5: Image Segmentation and Object Detection
Objective:
Master advanced computer vision techniques by implementing image segmentation and object detection algorithms. Gain experience with U-Net for pixel-wise segmentation and YOLO/Faster R-CNN for detecting objects in real-time applications, crucial for tasks like autonomous driving and medical image analysis.
Topics Covered:
- U-Net, YOLO, and Faster R-CNN
- Practical: Implement U-Net for segmentation and YOLO for object detection on a video feed
Capstone Project
Objective:
Apply the concepts learned throughout the course to develop a real-time object detection system for a practical application, such as surveillance or autonomous driving. This project will synthesize skills in CNNs, segmentation, and object detection to solve a real-world problem.
Frequently Asked Questions
1. Are there live sessions or is the course self-paced?
The course is primarily self-paced, with access to pre-recorded video lectures and hands-on coding exercises. There are optional live sessions and Q&A sessions where you can interact with instructors and get help on specific topics or challenges.
2. What tools and software do I need for this course?
You will be using Python and popular libraries such as OpenCV, TensorFlow/Keras, and PyTorch. These are free and open-source tools. A computer with internet access is required, and for more complex tasks, having access to a GPU for faster processing is beneficial.
3. Will I have access to course materials after the course ends?
Yes, you will have lifetime access to the course materials, including video lectures, assignments, and downloadable resources. This allows you to revisit the content whenever you need to refresh your knowledge.
4. What kind of support will be available?
You will have access to a support community where you can ask questions and engage in discussions with peers and instructors. Additionally, instructors will hold live Q&A sessions to assist with challenging topics.
5. What career opportunities can this course lead to?
After completing this course, you will be qualified for roles such as Computer Vision Engineer, Machine Learning Engineer, AI Developer, Data Scientist, or Research Scientist in computer vision, with applications across industries like healthcare, autonomous vehicles, retail, security, and entertainment.
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