Computer Vision for AI Professionals - eLearning
450,00 EUR
- 30 hours
Unlock the power of visual intelligence with the Computer Vision for AI Professionals Training, designed to help you build systems that can see, interpret, and understand the world like humans. This course introduces you to the core concepts and practical applications of computer vision — a key branch of artificial intelligence used in autonomous vehicles, healthcare imaging, facial recognition, robotics, and smart surveillance.
Key Features
Language
Course and material in English
Level
Intermediate - Advanced level
Access
1 Year access to the learning platform
5 Hours of On-Demand Videos
with 10+ hours recommended study time
22 Guided Hands-On Exercises
5 Auto-Graded Assessments
13 Recall Quizzes
3 Comprehensive Assignments
Certificate
Program completion certification included
Learning Outcomes
At the end of this Course, you will be able to understand:
Fundamentals
Understand the fundamentals of image processing and different image types
Histogram
Create color histograms and explore intensity transformations and gamma correction
Softmax
Learn the softmax function and key challenges in image classification
Explore
Explore edge, shape, and corner detection techniques
Deep Learning
Apply deep learning methods for accurate image recognition
YOLO
Work with YOLO and gain a basic understanding of image segmentation
Course timeline
Introduction to Image Processing
Lesson 01
- Introduction to Image Processing
- Digital Image Processing
- Types of Images
- Coordinate Schemes and RGB
- Other Color Schemes
- Histogram and Statistics
- Intensity Transforms and Gamma
- Blending
- Convolution
- Edge Detection
- Smoothing and Sharpening
- Morphological Filters
Classification
Lesson 02
- Challenges in Image Classification
- Traditional Imaging Workflow
- Deep Learning Components for Feedforward Networks
- Deep Learning Function and Universal Approximation
- Softmax Function
- Issues with Feed Forward Size
- Bias-Variance and Overfitting
- Plot Model History
- Save and Load Models
CNN
Lesson 03
- Feedforward Challenges and Rise of CNN
- Convolutions for CNNs
- Multiple Channels and Outputs in CNNS
- CNN Dimensions-Color
- Max Pooling
- Putting the CNN Components Together
- CFAR 10 CNN with TensorFlow Datasets
Improving CNN
Lesson 04
- Data Augmentation
- Affine Transformations
- Transfer Learning
- More on Transfer Learning
- Transfer Learning Implementation
- Different Architectures for Transfer Learning
- Future of Deep Learning
Segmentation and Object Recognition
Lesson 05
- Segmentation With Thresholding
- Segmentation With Clustering
- Segmentation With CNN
- Segmentation With U-Net
- Image Segmentation With U-Net
- U-Net Model
- Object Localization
- Multiple Objects Classification Challenges
- YOLO
Who Should Enroll in This Program?
AI and Machine Learning professionals
Data scientists interested in image and video analytics
Software engineers transitioning into AI roles
Developers working in robotics, automation, or IoT
Professionals in healthcare, security, or automotive industries
Students and tech enthusiasts exploring advanced AI applications
Prerequisites
- Basic knowledge of Python programming
- Fundamental understanding of machine learning concepts
- Familiarity with data science basics (helpful but not mandatory)
- Basic understanding of linear algebra, probability, or statistics (recommended)
- No prior computer vision experience is required..
Statements
Licensing and accreditation
This course is offered according to Partner Program Agreement and complies with the License Agreement requirements
Equity Policy
Candidates are encouraged to reach out to AVC for guidance and support throughout the accommodation process.
Frequently Asked Questions
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