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  • Computer Vision

Computer Vision

  • By educlassixglobal
  • (1 Rating)
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    • Learn how computers interpret visual data. Build models for object detection, image classification, and facial recognition using deep learning frameworks and real world datasets

      What Will You Learn?
      • Image processing basics
      • CNN model building
      • Object detection skills
      • OpenCV with Python
      • Vision AI projects
      • Real-world datasets

      Material Includes

      • Get access to curated notes, guides, and reference materials that help you revise concepts anytime, even offline
      • Work on practical assignments and real-life scenarios to build job-ready skills and strengthen your portfolio
      • Test your understanding with interactive quizzes and module-based assessments to track your progress and reinforce learning
      • Practice what you learn with guided exercises that help you gain confidence in applying concepts independently

      Requirements

      • Basic understanding of Python programming.
      • Familiarity with fundamental mathematics and linear algebra (helpful but not mandatory).
      • No prior experience in deep learning required — key concepts will be covered in the course.
      • A passion for artificial intelligence and problem-solving using visual data.

      Course Content

      Introduction to Computer Vision & OpenCV

      • Applications
      • Reading & writing images

      Basic Image Operations

      • Drawing
      • Resize
      • Crop
      • Histograms

      Image Processing

      • Blurring
      • Thresholding
      • Edge Detection

      Morphology

      • Erosion
      • Dilation
      • Opening
      • Closing

      Contours & Shape Detection

      • Finding contours
      • Shape approximation

      Color Spaces

      • RGB transformation
      • HSV transformation
      • LAB transformation

      Introduction to CNN for Computer Vision

      • CNNs applied to image classification

      Transfer Learning in Computer Vision

      • Pretrained networks for classification

      Object Detection Introduction

      • YOLO concepts
      • SSD
      • Faster-RCNN

      Real-time Detection

      • Real-time webcam detection using YOLOv5

      Segmentation

      • Semantic segmentation with U-Net

      Instance Segmentation

      • Mask-RCNN overview

      Face Detection

      • Haar cascades
      • DNN-based detection

      Face Recognition

      • DeepFace
      • FaceNet basic pipeline

      Image Captioning

      • CNN + RNN approach

      Vision Transformers

      • Vision Transformer (ViT) introduction
      • DETR introduction

      Deployment of Computer Vision Apps

      • Flask-based deployment
      • Streamlit deployment

      Computer Vision Interview Prep

      • Discussion
      • Resume feedback

      Tags

      • Health
      • Kitchen

      A course by

      E
      educlassixglobal

      Student Ratings & Reviews

      5.0
      Total 1 Rating
      5
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      1
      0 Rating
      2 years ago
      The assignments were challenging but rewarding.

      Course Includes:

      • Price:
        $399.00
      • Instructor:educlassixglobal
      • Duration: 25 hours 52 minutes
      • Lessons:37
      • Students:1
      • Level:Beginner
      $399.00
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