Virtual Training
Data Science, AI, and Advanced Analytics
Computer Vision Fundamentals Online Course
Join our virtual, live instructor-led session and master Computer Vision Fundamentals Training from anywhere in the world.
5 Days
Duration
Live Online
Delivery
12 Dates
Available
Certificate
Included
Master Computer Vision Fundamentals Training to build robust image processing pipelines, deploy deep learning models, and automate visual inspection using OpenCV and TensorFlow.
Upcoming Virtual Training Schedules
Join from anywhere in the world with our live instructor-led sessions
| Code | Start Date | End Date | Duration | Fee | |
|---|---|---|---|---|---|
| CVF-05 | Mon - Fri (5 Days) | USD 1,050 | Reserve my seat → Register my team → | ||
| CVF-05 | Mon - Fri (5 Days) | USD 1,050 | Reserve my seat → Register my team → | ||
| CVF-05 | Mon - Fri (5 Days) | USD 1,050 | Reserve my seat → Register my team → | ||
| CVF-05 | Mon - Fri (5 Days) | USD 1,050 | Reserve my seat → Register my team → | ||
| CVF-05 | Mon - Fri (5 Days) | USD 1,050 | Reserve my seat → Register my team → | ||
| CVF-05 | Mon - Fri (5 Days) | USD 1,050 | Reserve my seat → Register my team → |
Here's What You'll Learn
Each module tackles real challenges you face in your role
1
Image Processing Foundations and OpenCV
2
Feature Engineering and Classical Vision
3
Deep Learning for Image Classification
4
Transfer Learning and Advanced Architectures
5
Object Detection and Localization Frameworks
6
Image Segmentation and Video Analysis
7
Vision Strategy and Model Deployment
Market-specific guidance for Canada
A country-aware view of the pressures, proof points, and practical tools that shape how this course applies locally.
Tools and platforms relevant to this field
6Field-relevant examples that may be featured in training where they support the confirmed scope. Exact coverage depends on participant needs and delivery format.
-
OpenCV Open Source Computer Vision LibraryUsed for image preprocessing, feature extraction, camera calibration, and classical vision workflows before or alongside deep-learning models.
-
TensorFlow GoogleUsed to build, train, and deploy convolutional neural networks for image classification, detection, and segmentation tasks.
-
PyTorch MetaUsed for rapid prototyping of computer vision models and experimentation with custom training loops and transfer learning.
-
ONNX Runtime MicrosoftUsed to run optimized inference across different hardware targets when moving models from training to production.
-
NVIDIA TensorRT NVIDIAUsed to accelerate inference on NVIDIA GPUs for low-latency vision applications such as inspection and tracking.
-
scikit-learn scikit-learn developersUsed for evaluation workflows, baseline models, and post-processing around computer vision pipelines.
Where this course runs
Computer Vision Fundamentals Training is delivered in the cities below — pick the one that fits your schedule.
