Why this course?

Description

AI Computer Vision Master class using OpenCV provides a comprehensive and hands-on learning experience for individuals interested in understanding and applying computer vision techniques. OpenCV, a popular open-source computer vision library, serves as the primary tool for this master class.

The master class typically covers essential concepts like image manipulation, filtering, and transformation, while also delving into advanced topics like machine learning-based object detection and facial recognition.

AI-Computer Vision Master class featuring Open CV! This immersive learning experience is designed to empower you with the knowledge and skills needed to excel in the dynamic field of computer vision.

Whether you're a beginner seeking an introduction to image processing or an experienced developer aiming to advance your capabilities, this master class is your gateway to mastering the powerful Open CV library. Understand the fundamentals of computer vision, its applications, and how it plays a crucial role in artificial intelligence.

Image Filtering: Understand the significance of filtering in image processing and how to apply various filters for noise reduction and feature enhancement.

Image Processing Basics: Explore fundamental image processing techniques, including filtering, transformations, and enhancement, to gain a solid foundation in image manipulation.
Advanced Topics in Computer Vision: Tackle sophisticated topics like machine learning-based object detection, facial recognition, and other cutting-edge applications that leverage the power of OpenCV
Prerequisites and Accessibility: Clear Prerequisites: Clearly defined prerequisites to help participants gauge their readiness for the master class.
Accessibility: Designed to accommodate a diverse audience, from beginners to experienced professionals.
 

Uses AI- Computer Vision Master Class (openCV):
The AI-Computer Vision Master class featuring OpenCV equips participants with skills and knowledge that find applications across various domains. Here are some key uses and applications:
 

Retail: Automate product detection and tracking on shelves for inventory management.
Manufacturing: Enhance quality control by identifying defects in products on the production line.

 

Healthcare: Analyze medical images for diagnostics, pathology, and surgery assistance. 
 

Entertainment: Implement image and gesture recognition for interactive gaming and virtual reality.
 

Diagnosis: Assist in medical image analysis for conditions like tumor detection and organ segmentation. 
 

Research: Support medical research through the analysis of complex biological images.

Manufacturing: Enhance robotic vision systems for tasks like pick-and-place in assembly lines.
Healthcare: Implement robots for surgery assistance and patient care.

Interactive Learning: Develop educational tools that use computer vision for interactive and engaging learning experiences. Assessment: Implement automated grading and assessment systems for educational purposes.
 

MODULES OF AI- Computer Vision Master class (openCV)

Module 1: Introduction to AI?
Module 2: Why Python for AI?
Module 3: Introduction to computer vision and its application
Module 4: Image & video processing using Computer vision
Module 5: Color tracking using OpenCV
Module 6: Face counting Using OpenCV
Module 7: Edge detection using Opencv
Module 8: Region of Interest using Opencv
Module 9: Lane detection using OpenCV
Module10: Interfacing Arduino with Arduino Cloud
Module11: Fire detection using OpenCV
Module12: Gesture detection using Sklearn and opencv
Module 13: Digital art using opencv
Module 14: Mouse control using gesture
Module 15: Drowsiness detection using eye aspect ratio
Module 16: Head pose detection using ML
Module 17: Object recognition using DNN and tensor flow
Module 18: Vehicle speed estimation using DNN
Module 19: Emotion recognition using deep learning
Module 20: Traffic sign recognition using deep learning
Module 21: Social distance monitoring using deep learning
Module 22: Optical Character Recognition
Module 23: Speech Recognition and Text-to-speech Conversion
Module 24: Speech emotion recognition using deep learning
Module 25: Introduction to NLP and its Applications
Module 26: Tokenizing using nltk
Module 27: What is stemming in NLP?
Module 28: Purpose of lemmatizing
Module 29: About chunking and its functions
Module 30: Intro to NLP using AWS Day
Module 31: Working with AI in Azure