Master Computer Vision Deep Learning- OpenCV, YOLO, ResNet

Master Computer Vision & Deep Learning: OpenCV, YOLO, ResNet, available at $54.99, has an average rating of 4.75, with 76 lectures, 3 quizzes, based on 32 reviews, and has 125 subscribers.

You will learn about Master the fundamentals of deep learning, including neurons, neural networks, and activation functions Discover the architecture and design of state-of-the-art object detection models, such as Faster R-CNN, RetinaNet, SDD, and YOLO Build a real-world object detection application to automatically detect license plate numbers using Faster R-CNN Learn about the architecture and design of image classification models, such as SVM, VGG-16, ResNet50, and InceptionV3 Develop an image classification application to detect and train traffic sign boards using SVM Train an image classification model using ResNet to classify 20 different sets of multiple images Understand the design of object tracking frameworks, such as Meanshift, SORT, and DeepSORT Build a solution to track football players using object tracking This course is ideal for individuals who are Software engineers who want to learn deep learning and computer vision to develop cutting-edge machine learning solutions. or Machine learning enthusiasts who want to develop a portfolio of industry-relevant projects or Data scientists who want to expand their skills and knowledge in deep learning and computer vision or Students who want to gain hands-on experience with deep learning and computer vision or Professionals who want to transition into a career in machine learning It is particularly useful for Software engineers who want to learn deep learning and computer vision to develop cutting-edge machine learning solutions. or Machine learning enthusiasts who want to develop a portfolio of industry-relevant projects or Data scientists who want to expand their skills and knowledge in deep learning and computer vision or Students who want to gain hands-on experience with deep learning and computer vision or Professionals who want to transition into a career in machine learning.

Enroll now: Master Computer Vision & Deep Learning: OpenCV, YOLO, ResNet

Summary

Title: Master Computer Vision & Deep Learning: OpenCV, YOLO, ResNet

Price: $54.99

Average Rating: 4.75

Number of Lectures: 76

Number of Quizzes: 3

Number of Published Lectures: 76

Number of Published Quizzes: 3

Number of Curriculum Items: 79

Number of Published Curriculum Objects: 79

Original Price: $119.99

Quality Status: approved

Status: Live

What You Will Learn

Master the fundamentals of deep learning, including neurons, neural networks, and activation functions

Discover the architecture and design of state-of-the-art object detection models, such as Faster R-CNN, RetinaNet, SDD, and YOLO

Build a real-world object detection application to automatically detect license plate numbers using Faster R-CNN

Learn about the architecture and design of image classification models, such as SVM, VGG-16, ResNet50, and InceptionV3

Develop an image classification application to detect and train traffic sign boards using SVM

Train an image classification model using ResNet to classify 20 different sets of multiple images

Understand the design of object tracking frameworks, such as Meanshift, SORT, and DeepSORT

Build a solution to track football players using object tracking

Who Should Attend

Software engineers who want to learn deep learning and computer vision to develop cutting-edge machine learning solutions.

Machine learning enthusiasts who want to develop a portfolio of industry-relevant projects

Data scientists who want to expand their skills and knowledge in deep learning and computer vision

Students who want to gain hands-on experience with deep learning and computer vision

Professionals who want to transition into a career in machine learning

Target Audiences

Software engineers who want to learn deep learning and computer vision to develop cutting-edge machine learning solutions.

Machine learning enthusiasts who want to develop a portfolio of industry-relevant projects

Data scientists who want to expand their skills and knowledge in deep learning and computer vision

Students who want to gain hands-on experience with deep learning and computer vision

Professionals who want to transition into a career in machine learning

Become a Deep Learning Master and Build Cutting-Edge Industry Solutions

Welcome to the cutting-edge world of Deep Learning and Computer Vision! Brace yourself for an exhilarating journey into the heart of technology, where innovation meets transformation. Our comprehensive course is not just a course; it’s your ticket to becoming a master in the realm of Machine Learning, with a special focus on Computer Vision. Get ready to embark on a transformative learning experience that will empower you to take on the future of technology with confidence and prepare you for a career in machine learning, with a focus on industry-relevant skills and projects.

Key Highlights of the Course:

1. Mastery Through Code Walkthroughs: You won’t just learn concepts; you’ll become an expert through extensive code walkthroughs. Every project is accompanied by a detailed code explanation, ensuring you understand not just what you’re doing but why you’re doing it.

2. Industry-Ready Projects: All six projects included in this course are not only industry-relevant but also fully functional. And if you encounter any challenges, rest assured that our dedicated support team is just a message away, with 24-hour assistance to address any issues you may face.

3. Laser-Focused Project Selection: We’ve carefully curated projects that are in high demand in today’s job market. Each project you complete adds a valuable skill to your arsenal, enhancing your employability and career prospects.

4. Unparalleled Coverage: This course offers an unparalleled depth of knowledge. Explore ten Object Detection models, grasp the intricacies of seven Image Classification Models, and gain expertise in three Object Tracking Models.

5. Key Topics and Projects: Here’s a glimpse of what you’ll conquer in this course:

Neural Networks, ANN, CNN, and Activation Functions – Lay the foundation for your journey.

Object Detection Models – From RCNN to YOLOV4, master the art of object detection.

Image Classification Models – Dive into SVM, Decision Trees, KNN, VGG16, ResNet50, InceptionV3, and EfficientNet.

Object Tracking Models – SOT, MOT, Meanshift, SORT, DeepSORT – unravel the secrets of object tracking.

6. Real-World Applications: Apply your newfound knowledge to create solutions for real-world challenges, such as License Number Plate Recognition and Traffic Sign Detection, using state-of-the-art techniques.

7. Sports Analytics: Elevate your skills by tracking and analyzing the movements of football players, bringing advanced sports analytics to life.

Don’t miss out on this opportunity to elevate your career and become a specialist in Machine Learning. Join us on this incredible journey and unlock your potential. The future of technology is here, and it’s yours to conquer. Enroll now and take your first step towards an exciting and rewarding future in Machine Learning and Computer Vision. Your journey to mastery begins today !

Course Curriculum

Chapter 1: Course Starter

Lecture 1: Learning Path

Lecture 2: Course Starter – How to approach the course

Lecture 3: Udemy Review

Chapter 2: Understanding Computer Vision and AI

Lecture 1: Objectives

Lecture 2: Artificial Intelligence Overview

Lecture 3: What is Computer Vision ?

Lecture 4: Image Basics

Chapter 3: Tools Setup

Lecture 1: Objectives

Lecture 2: Tools Setup – Ubuntu

Lecture 3: Tools Setup – Windows

Lecture 4: Using Pycharm for Coding

Lecture 5: Using Jupyter Notebook and Shortcuts

Lecture 6: Using Google Colab

Chapter 4: Neuron, Neural Network and Activation Function

Lecture 1: Objectives

Lecture 2: What is a Neuron?

Lecture 3: Neuron Architecture

Lecture 4: Artificial Neural Network

Lecture 5: Convolutional Neural Network

Lecture 6: Activation Function

Chapter 5: Object Detection – R-CNN, FAST R-CNN, RPN, FASTER R-CNN and R-FCN

Lecture 1: Objectives

Lecture 2: Object Detection Overview

Lecture 3: Object Detection Architecture

Lecture 4: Object Detection vs Object Tracking

Lecture 5: R-CNN MODEL

Lecture 6: FAST R-CNN MODEL

Lecture 7: Region Proposal Network (RPN)

Lecture 8: FASTER R-CNN MODEL

Lecture 9: R-FCN MODEL

Chapter 6: Project 1 – Object Detection using Faster R-CNN

Lecture 1: Objectives

Lecture 2: Project Overview

Lecture 3: Code Walkthrough

Lecture 4: Code Download Instructions

Chapter 7: Object Detection – RetinaNet, SSD, YOLO, YOLOV3, YOLOV3 Tiny and YOLOV4

Lecture 1: Objectives

Lecture 2: RetinaNet

Lecture 3: SSD MODEL

Lecture 4: YOLO V3 Model

Lecture 5: YOLO V3 TINY MODEL

Lecture 6: YOLOV4 Model

Chapter 8: Project 2 – License Number Plate Recognition using YOLOV3

Lecture 1: Objectives

Lecture 2: Project Overview

Lecture 3: Code Walkthrough

Lecture 4: Code Download Instructions

Chapter 9: Project 3 – YOLOV3 Training for License Number Plate

Lecture 1: Objectives

Lecture 2: Project Overview

Lecture 3: Code Walkthrough

Lecture 4: Code Download Instructions

Chapter 10: Image Classification Models – SVM, Decision Tree, KNN

Lecture 1: Objectives

Lecture 2: Image Classification Overview

Lecture 3: Image Classification Pipeline

Lecture 4: Support Vector Machine(SVM)

Lecture 5: Decision Tree

Lecture 6: K Nearest Neighbor(KNN)

Chapter 11: Project 4 – Traffic Sign Detection and Training using SVM

Lecture 1: Objectives

Lecture 2: Project Overview

Lecture 3: Code Walkthrough

Lecture 4: Code Download Instructions

Chapter 12: Image Classification Models – VGG-16, ResNet50, Inceptionv3, EfficientNet

Lecture 1: Objectives

Lecture 2: VGG-16 Model

Lecture 3: ResNet50 Model

Lecture 4: Inceptionv3 Model

Lecture 5: EfficientNet Model

Chapter 13: Project 5 – Training ResNet Model for Image Classification

Lecture 1: Objectives

Lecture 2: Project Overview

Lecture 3: Code Walkthrough

Lecture 4: Code Download Instructions

Chapter 14: Object Tracking – SOT, MOT, Meanshift, SORT and DeepSORT

Lecture 1: Objectives

Lecture 2: Object Tracking

Lecture 3: Single Object Tracking and Multiple Object Tracking

Lecture 4: Meanshift Algorithm

Lecture 5: SORT Framework

Lecture 6: DeepSort Framework

Chapter 15: Project 6 – Tracking Football Players using Object Tracking

Lecture 1: Objectives

Lecture 2: Project Overview

Lecture 3: Code Walkthrough

Lecture 4: Code Download Instructions

Chapter 16: The Way Forward

Lecture 1: More Learnings

Instructors

Vineeta Vashistha
Technical Architect – Deep Learning

Rating Distribution

1 stars: 2 votes

2 stars: 0 votes

3 stars: 4 votes

4 stars: 4 votes

5 stars: 22 votes

Frequently Asked Questions

How long do I have access to the course materials?

You can view and review the lecture materials indefinitely, like an on-demand channel.

Can I take my courses with me wherever I go?

Definitely! If you have an internet connection, courses on Udemy are available on any device at any time. If you don’t have an internet connection, some instructors also let their students download course lectures. That’s up to the instructor though, so make sure you get on their good side!