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CODE: NAI07
DURATION: 3 Days | 5 Days | 10 Days
CERTIFICATIONS: CPD
This course provides a comprehensive introduction to deep learning techniques for image classification and object detection, two of the most impactful applications of computer vision. Participants will explore fundamental concepts of convolutional neural networks (CNNs), transfer learning, and state of the art architectures such as ResNet, YOLO, and Faster R-CNN. Through a blend of theory and hands-on practice, participants will gain practical skills in building, training, and deploying models capable of identifying, classifying, and detecting objects in real world images across diverse domains.
This course is available in the following formats:
Virtual
Classroom
Request this course in a different delivery format.
Course Outcomes
Delegates will gain the knowledge and skills to:
Understand the principles of CNNs and their role in image recognition tasks.
Apply transfer learning and fine-tuning to improve model performance.
Implement popular image classification and object detection algorithms.
Evaluate and optimize models using performance metrics and real-world datasets.
Deploy deep learning models for practical applications in various industries.
At the end of this course, you’ll understand:
This course is designed for data scientists, machine learning engineers, software developers, and researchers who want to specialize in computer vision applications. It is also suitable for professionals in industries such as healthcare, security, manufacturing, agriculture, and autonomous systems, where image-based analysis and object detection play a critical role. A basic understanding of Python and machine learning concepts is recommended.
✓ Modern facilities
✓ Course materials and certificate
✓ Accredited international trainers
✓ Training materials and workbook
✓ Access to online resources
