Learn OpenCV: Build # 30 Apps with OpenCV, YOLOv8 & YOLO-NAS

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Udemy Coupon Code Learn OpenCV: Build # 30 Apps with OpenCV, YOLOv8 & YOLO-NAS Course

OpenCV, Object Detection, Object Tracking, Object Segmentation, YOLOv8, YOLO-NAS, Train Custom Dataset, Pose Estimation

Created by Muhammad Moin | 24.5 hours on-demand video course | 64 downloadable resources

What you’ll learn

  • Understand basics of OpenCV
  • Use OpenCV to work with Image and Video Files
  • Apply different image processing techniques with OpenCV including Blurring, Dilation, Erosion, Edge Detection, Finding and Drawing Contours, Warp Perspective
  • Use Haar Cascades Classifiers to Detection Face, License Plate etc
  • Use OpenCV to create Real-World Applications including Optical Mark Recognition, Lane Detection, QR & Bar Code Detection, Object Size Measurement etc
  • Use OpenCV to create Advanced Projects/ Applications including Basket Ball Shot Predictor, Parking Space Counter, Pong Game using Hand Gestures, Gesture Vol Cnt
  • Understand the fundamentals of Object Detection and learn how to use YOLO Algorithm to do Object Detection with YOLOv8 and YOLO-NAS
  • Understand the basics of Object Segmentation and learn how to do Object Segmentation with YOLOv8 and how to train YOLOv8 Segmentation Model on Custom Data
  • Understand the Basics of Object Tracking and how to integrate the SOTA Object Tracking Algorithms i.e. SORT and DeepSORT with YOLOv8 and YOLO-NAS
  • Use Object Detection and Object Tracking Algorithms to create different Real World Applications including Vehicles Counting (Entering & Leaving) using YOLO-NAS
  • Learn how to integrate Object Tracking with an Object Detection Model trained on a Custom Dataset
  • Learn how to detection Grocery Items in a Retail Store with YOLO-NAS.
  • Understand the Segment Anything Model (SAM) and how to do Image and Video Segmentation with YOLO-NAS and Segment Anything Model (SAM)
  • Understand the Basics of Pose Estimation and Learn how to implement Pose Estimation using OpenCV and MediaPipe
  • Create Real World Applications using OpenCV and MediaPipe including Bicep Curl Counter and Push-Ups Counter
  • Build Real World Applications with YOLOv8 and YOLO-NAS including Potholes Detection, Personal Protective Equipment Detection, Vehicles Intensity Heatmaps etc
  • Learn Optical Character Recognition and create different apps i.e. License Plate Detection and Recognition, Multi-Cam License Plate Detection and Recognition,

Learn OpenCV: Build # 30 Apps with OpenCV, YOLOv8 & YOLO-NAS Course

Welcome to the Learn OpenCV: Build # 30 Apps with OpenCV, YOLOv8 & YOLO-NAS Course, we will start with the basics of OpenCV. From there, we’ll dive into creating real-world applications using OpenCV. Next up, we’ll explore different Object Detection Algorithms i.e. YOLOv8 and YOLO-NAS. We will create different applications using YOLOv8 and YOLO-NAS. In this course, we’ll not only implement Object Tracking from scratch using OpenCV but also explore top-notch Object Tracking Algorithms like SORT and DeepSORT. Moreover, we’ll also focus on Pose Estimation in this course as well. With the help of MediaPipe and OpenCV, we’ll uncover the secrets of estimating poses. We’ll apply this knowledge to develop practical applications, such as a Bicep Curl Counter and Push-Ups Counter, bringing your skills to life.

Learn OpenCV: Build # 30 Apps with OpenCV, YOLOv8 & YOLO-NAS Coupon Code

Learn LangChain: Build #22 LLM Apps using OpenAI & Llama 2

Build Real World LLM powered applications with LangChain, OpenAI, Llama2, Hugging Face. Create Web Apps with Streamlit.
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YOLOv7 YOLOv8 YOLO-NAS: Object Detection, Tracking & WebApp Best seller

YOLOv7 YOLOv8 YOLO-NAS Object Detection, Segmentation, Tracking, Pose Estimation, Web App Development, Real World 22 + …
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Who this course is for:

  • For Everyone who is interested in Computer Vision
  • For Everyone who wants to learn the OpenCV and latest YOLOv8 and YOLO-NAS version
  • For Everyone who study Computer Vision and want to know how to use YOLO for Object Detection
  • For Everyone who aims to build Deep learning Apps with Computer Vision