AI with Computer Vision Specialization Program
Unlock the full potential of computer vision with our comprehensive, project-driven course designed to take you from foundational principles to cutting-edge techniques. Whether you're new to the field or aiming to deepen your expertise, this course provides a robust pathway to mastering computer vision with real-world applications, hands-on coding, and industry-relevant tools.
Who Should Enroll?
This course is ideal for:
- Aspiring computer vision engineers and machine learning enthusiasts.
- Software developers seeking to integrate vision capabilities into real-world applications.
- Data scientists wanting to handle image-based data more effectively.
- Students or professionals transitioning into AI, deep learning, or robotics.
No prior experience in computer vision is required—just a solid understanding of Python and a passion for building intelligent visual systems.
What You'll Learn
Progress from the fundamentals of image processing to advanced deep learning models and 3D vision systems, all through hands-on exercises and guided projects:
- Image representation, enhancement, and filtering using OpenCV.
- Classical techniques like segmentation, HOG, and SVM-based detection.
- Deep learning models such as CNNs, YOLO, R-CNN, and U-Net.
- Cutting-edge techniques like Vision Transformers (ViT) and 3D object recognition.
- Real-world computer vision system design and implementation.
Learning Outcomes
By the end of this course, you will be able to:
- Build and optimize image processing pipelines using OpenCV.
- Implement object detection and image segmentation models using both traditional and deep learning methods.
- Apply and fine-tune pre-trained CNN architectures for classification tasks.
- Leverage 3D computer vision for recognition, segmentation, and reconstruction.
- Design, develop, and deploy your own end-to-end computer vision applications.
Real-World Projects Include
Throughout the course, you'll work on practical, real-world projects such as:
- Facial Recognition System
- Autonomous Navigation System for Robotics
- Object Tracking Application
- Medical Image Analysis System
- 3D Point Cloud-Based Recognition
All projects are geared toward real software development scenarios, emphasizing robust code and scalable system design.
Course Curriculum
-
Introduction to Computer Vision
-
What is Computer Vision?
-
History of Computer Vision
-
Applications of Computer Vision
-
Image Processing Fundamentals
-
Image Representation (pixels, channels)
-
Basic Image Operations (filtering, transformations)
-
Image Enhancement (contrast, color)
-
Feature Extraction (edges, corners)
-
Practical: Implementing image processing operations using OpenCV
-
Image Segmentation
-
Thresholding
-
Region-based Segmentation
-
Clustering-based Segmentation
-
Object Detection
-
Haar Cascades
-
HOG (Histogram of Oriented Gradients)
-
SVM for Object Detection
-
Practical: Building a simple object detection system using Haar cascades
-
Introduction to Deep Learning for Computer Vision
-
Review of Convolutional Neural Networks (CNNs)
-
Common CNN Architectures (AlexNet, VGG, ResNet)?
-
Transfer Learning25:05
-
Advanced CNN Architectures
-
Inception
-
MobileNet
-
EfficientNet
-
Practical: Implementing image classification using a pre-trained CNN
-
Object Detection with Deep Learning
-
R-CNN family (R-CNN, Fast R-CNN, Faster R-CNN)
-
YOLO (You Only Look Once)
-
SSD (Single Shot MultiBox Detector)
-
Image Segmentation with Deep Learning
-
Fully Convolutional Networks (FCNs)
-
U-Net
-
Transformers in Vision (ViT)
-
Practical: Implementing an object detection or image segmentation model
-
Introduction to 3D Computer Vision
-
3D Data Representation (Point Clouds, Voxels, Meshes)
-
3D Transformations and Coordinate Systems
-
3D Object Recognition
-
3D Shape Descriptors
-
Deep Learning for 3D Object Recognition
-
3D Scene Understanding
-
T3D Reconstruction
-
3D Semantic Segmentation
-
Practical: Working with 3D point cloud data and implementing a basic 3D recognition task
-
Students will unview several hands-on projects throughout the course, culminating in a final project where they design and implement a computer vision system from scratch.
-
Project examples:
-
Developing an object tracking application
-
Creating an autonomous navigation system for a robot
-
Implementing a system for medical image analysis
Jonathan Campbell
- 72 Videos
- 102 Lectures
- Exp. 4 Year
At vero eos et accusamus et iusto odio dignissimos ducimus qui blanditiis praesentium voluptatum deleniti atque corrupti quos dolores et quas molestias excepturi.
Item Reviews - 3
Submit Reviews
PLACEMENT ASSISTANCE
*MIN 5 COMPANY WALK-INS
GitHub portfolio and Job-ready resume to enhance their career prospects.
- Resume & Linkedin Building
- 1-1 Mock Interviews
- 100% Hands-on
- Certification
Tools Covered
Master industry-standard tools and frameworks, including:
OpenCV
TensorFlow
PyTorch
ResNet
VGG
EfficientNet
YOLO
R-CNN
SSD
U-Net
FCNs
3D Vision Tools
Explore Top Categories
Unlock your potential with our live interactive classes taught by industry experts. Get your doubts clarified instantly and ensure you understand every concept thoroughly.
Our Students Reviews
Lorem ipsum dolor sit amet, consectetur adipiscing elit, sed do eiusmod tempor incididunt ut labore et dolore magna aliqua. Ut enim ad minim veniam.
Josaph Manrty27 Oct 2019
" Commodo est luctus eget. Proin in nunc laoreet justo volutpat blandit enim. Sem felis, ullamcorper vel aliquam non, varius eget justo. Duis quis nunc tellus sollicitudin mauris. "
Rita Chawla2 Nov May 2019
" Commodo est luctus eget. Proin in nunc laoreet justo volutpat blandit enim. Sem felis, ullamcorper vel aliquam non, varius eget justo. Duis quis nunc tellus sollicitudin mauris. "
Adam Wilsom10 Nov 2019
" Commodo est luctus eget. Proin in nunc laoreet justo volutpat blandit enim. Sem felis, ullamcorper vel aliquam non, varius eget justo. Duis quis nunc tellus sollicitudin mauris. "