Computer Vision with Deep Learning

Learn Object Detection and Deep Learning Concepts with YOLO, SSD, SVM, ResNet50, Inceptionv3 Models and CNN architecture

What you'll learn:
Understand Deep Learning basics – Neuron, Neural Network and Activation Function
Learn architecture and design of Object Detection Models like Faster R-CNN, RetinaNet, SDD, YOLO etc
Develop Object Detection applications like Automatic License Number Plate etc
Learn architecture and design of Image Classification Models such as SVM, VGG-16, ResNet50, InceptionV3 etc
Develop Image Classification applications like Traffic Sign Board Detection etc
Learn design of Object Tracking Frameworks like Meanshift, SORT and DeepSORT

Requirements:
Basic Programming skills in Python
No prior knowledge of Mathematics or Rigorous Coding

Description:
Stay up-to-date with the leading-edge in the Machine Learning and develop an industry portfolio through this course by learning Computer Vision and Deep Learning foundational concepts, Object Detection, Image Classification and Object Tracking.
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 Projects we will be designing:
Use pre-trained Faster R-CNN model to do object detection in a video
Develop Object Detection Application Automatic Number Plate Detection
Build and Train YOLOV3 based Object Detection Model for License Number Plate Detection for cars
Use SVM model to classify and label traffic signs in a video
Build and Train ResNet based Image Classification Model for identification of 20 different types of classes
Design Football Playing Object Tracking application using SORT and YOLO

Who this course is for:
Deep Learning Enthusiasts who want to train models
Python Developers who want to develop AI solutions
Computer Vision Professionals
Machine Learning Developers
Data Scientist

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