Computer Vision with Deep Learning, OpenCV, YOLO, ResNet50

Develop Expertise in Computer Vision and Deep Learning foundational concepts, Object Detection, Image Classification and Object Tracking and develop an industry portfolio with the leading-edge in the Machine Learning through this course.

This course stands apart in its league of courses because of: Computer Vision with Deep Learning, OpenCV, YOLO, ResNet50 Udemy

• Detailed Code Walkthrough for all the 6 projects

• All projects are in working condition and support is provided within 24 hours for any issues faced

• Selective choice of projects that are in huge demand in industry

• Comprehensive Coverage of 10 Object Detection models

• Clear Explanation of 7 Image Classification Models

• Imparting knowledge on 3 Object Tracking Models

  
  

The recent innovations of Machine Learning technology have brought in huge technological transformation and most of the business are now shifting towards technology-enabled business models fueled by Deep Learning and Computer Vision. To maintain competitiveness in the industry, it is very important to stay up to date and build expertise on these skills.

The course has been designed to empower you with the core concepts of Computer Vision and Deep Learning with neural network, ANN, CNN along with activation function. After covering these basics, the course explains in detail the object detection architecture, illustrates how it is different from object tracking and then details out the widely used object detection models as they have evolved over time. To begin with, we start with the architecture design of R-CNN Model and then move on to FAST R-CNN Model which is advanced version of R-CNN. Thereafter, we explain the concept of Region Proposal Network (RPN) and then leverage it to build FASTER R-CNN MODEL and close this legacy with R-FCN Model. Moving on, the course dives deep into advanced object detection models starting with Retinanet, SSD and then covering the YOLO series in which we are talking about YOLO V3, YOLO V3 Tiny and YOLOV4 Model.

Thereafter, we move on the next logical step of Image Classification as the output of detected objects is consumed by image classification models for better identification of input data. We will start with basic machine learning image classification algorithms like Support Vector Machines (SVM), Decision Tree and K Nearest Neighbor (KNN) and then move on to advanced algorithms such as VGG-16, ResNet50, Inceptionv3 and EfficientNet Model.

Towards the end, we will move on to final concept of Object Tracking where after identification of objects in a video, we start tracking it as the video process. Within Object Tracking, we will cover Meanshift Algorithm, SORT and DeepSort Framework.

The course has been designed to explain deep learning and computer vision concepts in depth by first explaining the technology concepts and then their implementation through code. Detailed code walkthrough has been included for all the code implementations in projects and source code is available for download. In addition to this, the quiz in the course helps you to assess your knowledge and identify the improvement areas.

Enroll in this course and become specialized in machine learning. Here are just few of the key topics we will be learning and projects that we will design in the course:

Installation Guidelines

• The course covers in detail the installation of Python and PyCharm along with packages both for Window and Linux based OS.

• Tips and shortcuts for usage of Jupyter Notebook along with Google Colab is also provided in detail.

Deep Leaning Basics Guidelines

• Overview of Artificial Intelligence & Computer Vision along with basic concepts required to work on Image is explained in detail.

• Deep Learning concepts of Neuron and its Architecture, ANN, CNN and Activation Function differences is covered with this course.

  
  

Object Detection

• The course explains the Object Detection Architecture and its difference with Object Tracking.

• The complete journey of how various architectures of CNN develop and their benefits over each other. Starting with R-CNN Model to identify objects present in image, the course moves on to explain FAST R-CNN Model and then the development of Region Proportional Model (RPN) concept. Finally, the FASTER R-CNN Model and R-FCN Models for Object Detection on Images or Videos are introduced. In order to explain these concepts with practical usage we have provided a project - Object Detection in a video with the help of Faster RCNN model.

• The course provides comprehensive coverage of most advance models in market for Object Detection both on Image and Videos. We start with RetinaNet which makes use of feature pyramid network to detect objects at multiple scales, then we introduce SSD Model which detects models in single, faster version for modern world. Thereafter we are introducing Yolo which predicts bounding boxes and class probabilities directly from full images in one evaluation making it much faster.

• In the course, we are explaining in detail Yolo V3 first and then Yolo V4 which is a more advanced version of Yolo and then Yolo V3 tiny which is light weight model of Yolo. To provide the practical implementation, we are developing - Object Detection Application for Automatic Number Plate Detection. Along with this we will also learn to build and train YOLOV3 based Object Detection Model for License Number Plate Detection for cars.

  
  

Image Classification

• We are then introducing the concept of Image classification. Object Detection and Image classification concepts usually go hand in hand as image filtering is usually performed with the help of Image classification in large variety of input so that we obtain better quality of Object detection in input image on selected group.

• The course explains in details the concepts of Machine Learning like Support Vector Machines (SVM), Decision Tree, and KNN methods for performing image classification. We are also designing SVM model-based project to classify and label - Traffic Signs in a Video.

• The course also explains advanced models of Deep Learning for Image Classification which includes VGG16, ResNet50, Inceptionv3 and recently introduced Efficient Model. To provide the practical implementation, we are also building and training a ResNet based Image Classification model for identification of 20 different types of classes.

  
  

Object Tracking

• The course explains the concepts of single and multiple object tracking with the help of Meanshift, SORT and Deep Sort algorithms.

• Object Tracking implementation for practical usage has been explained through our last project - Design Football Playing Object Tracking application using SORT and YOLO.