As advancements in technology continue, machine learning continues to shape industries across the board. One of the most relevant applications of machine learning is computer vision, which enables computers to interpret visual data from the world around them. Computer Vision with Deep Learning, OpenCV, YOLO, ResNet50 is one course that intends to educate individuals on this technology.
Overview of the Course
This course is designed to provide a comprehensive understanding of deep learning and computer vision concepts, including object detection, image classification, and object tracking. With this knowledge in hand, students can acquire the skills they need to develop a portfolio of products and applications to use in current or future industry settings. The course is designed to educate students on these topics through hands-on experiences and tutorials.
Course Description
As noted previously, this course is all about teaching individuals about deep learning and computer vision. Instructors will walk learners through the concepts of these technologies, and then they will proceed to delve deeper on the topic of object detection. Throughout the course, learners will work on six distinct projects with source code available for download. What sets this course apart from others is the selective inclusion of those projects, which are in high demand in the industry.
One of the things that makes this course a great choice for learners is the detailed code walkthrough that accompanies each project. Instructors provide support within 24 hours for any issues learners might face. Ultimately, students will become specialized in machine learning and grow comfortable working with many models.
What You Will Learn
There are a variety of topics that students will learn in this course. Participants can expect to strengthen their knowledge of neural networks like ANN and CNN, as well as take a deep dive into the activation function. This course will introduce learners to ten object detection models, including RCNN, Fast R-CNN, Faster R-CNN, and R-FCN. Students will also learn object detection models for RetinaNet, SSD, YOLO, YOLOV3, YOLOV3 Tiny, and YOLOV4.
In addition to models for object detection, this course covers seven distinct image classification models. Students will learn support vector machines and decision trees, KNN, and some of the most popular models around. Among these is VGG16, ResNet50, InceptionV3, and EfficientNet.
The course rounds out its offerings with three different object tracking models. Participants will learn about SOT, MOT, Meanshift, SORT, and DeepSORT. To put the skills that they have learned into practice, participants will also learn how to use the faster R-CNN solution for object detection, the YOLOv3 solution plate recognition, and much more.
Course Stats
The course has received consistently high reviews, with an aggregate rating of 4.45026 across the 20 total reviews as of its submission for this article. This course has attracted individuals who are interested in the technology industry and looking to increase their knowledge of computer vision.
Why Choose Computer Vision with Deep Learning, OpenCV, YOLO, ResNet50?
This course stands apart by providing a comprehensive description of deep learning and computer vision concepts. The selective choice of the projects means students will learn how to work with popular models that are in high demand on the job market today.
The course's instructors provide learners with support within 24 hours for any issues learners might face. The code walkthrough that accompanies each project is highly detailed, which helps ensure students can learn from different code implementations.
The Bottom Line
Computer Vision with Deep Learning, OpenCV, YOLO, ResNet50 is an excellent course for individuals who want to gain a comprehensive understanding of different aspects of machine learning and put what they learn into practice. The objective of the course is to empower learners with the necessary foundational knowledge to work with popular models used widely in the industry. The code walkthrough helps ensure a strong understanding of these models, and instructor support helps learners overcome any roadblocks.
