Overview What You Will Learn Curriculum Instructor

Course Overview

Custom object detection is a trending area in computer vision, driven by thousands of emerging use cases and a wide range of real-world applications. A neural network with some level of complexity, usually at least two layers, qualifies as a deep neural network (DNN), or deep net for short. Deep nets process data in complex ways by employing sophisticated mathematical modeling. YOLO, also known as You Only Look Once, is one of the most powerful real-time object detection algorithms. It is called that way because, unlike previous object detection algorithms such as R-CNN and its upgrade, Faster R-CNN, it only needs the image or video to pass through its network once.

In this course, you’ll understand the mathematical intuition behind YOLO (You Only Look Once) and learn how to work with the right data while building custom models for object detection. You will explore different YOLO models and discuss, through real-time implementations, which models to choose and which to avoid. You will also build real-time, production-based solutions using YOLO and develop customized, production-ready object detection models.

By the end of this course, you’ll master the architecture and mathematics behind YOLO, gain an understanding of object detection models, learn how to label your data, and train your own custom object detection model using YOLO.

What You Will Learn

  • You will learn about object detection models and data collection & annotation.
  • You will learn how to label your data and train your custom object detection model using YOLO.
  • You will also learn which yolo version is suitable for industrial project and why.

Program Curriculum

  • Introduction to Object Detection
  • Applications of Object Detection
  • Different Object Detection Algorithms
  • $7 Million Cybersecurity Scholarship by EC-Council
  • Chapter 1 Quiz

  • How to Collect Data and Difficulties with Real Time Scenarios?
  • Image Scrapping for Creating Data-set
  • Tools to Annotate Data
  • Chapter 2 Quiz

  • What Is a Sliding Window & Implementation
  • Drawbacks of Sliding Window
  • Introduction to YOLO
  • Chapter 3 Quiz

  • Intersection Over Union
  • Non-Max Suppression and Anchor Boxes
  • YOLO Loss Function & MAP
  • Complete YOLO Algorithm
  • Chapter 4 Quiz

  • Setting up Training Environment
  • How To Train Deep Learning Model?
  • Chapter 5 Quiz

  • Overview of Training Model
  • Step by Step Training with YOLOv4
  • Step by Step Training with YOLOv4 (Getting Assertion Error)
  • Step by Step Training with YOLOv4 (Understanding Assertion Error & Training Your Model)
  • Chapter 6 Quiz

  • Testing with Darknet
  • Testing with OpenCV DNN
  • Course Handbook
  • Chapter 7 Quiz
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Instructor

Vivek Chaudhary chaudhary

Vivek Chaudhary currently works as a freelance data scientist and has worked with different product-based & EdTech startups. He has published one of the best-selling books on Amazon, “Data Investigation-EDA the right way”. His areas of expertise are applied statistics, EDA, data cleaning techniques, and feature engineering and process to building statistical models. According to him “Building Assumptions” is the important factor to apply statistical tools in real-time. If you can’t build assumptions, then no matter how much you learn at the end, it will be difficult to apply statistical techniques. He has mentored 200+ professionals to start their journey and helped them understand applied statistics & EDA.

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