Computer vision with embedded Machine Learning courses is an online learning course where you study computer vision and Machine Learning while developing your project. This might include videos, reports, articles, etc. It then moves into how machine learning works and its implementation for computer vision applications. The goal of the course is to teach students about statistical learning theory and give them ideas on how they can apply this theory to their work in Machine Learning or Computer Vision applications. You are required to build a mobile app that would demonstrate your understanding of one or more of the topics touched upon in this Course and teach others about what you have learned as well!
The course syllabus details what you will learn from this online training program.
Introduction to Machine Learning and Computer Vision Applications. This section of the Course will discuss what machine learning is in computer vision, how it works, and how you can use it to solve problems in your projects. You will learn about implementing random forest and gradient boosted decision trees and fitting them using the scikit library. This section covers data exploration, feature selection, feature filtering, attribute weighting, and transformation. We will also talk about Big Data Analysis Methods such as clustering, dimensionality reduction, and pre-processing features. This course is targeted at beginners interested in learning about Machine Learning, Computer vision, and Embedded Machine Learning all at once. There are many online platforms which sell courses online. To reduce the number of videos we need to cover, only covering topics from each area that we think are most important within this Course would be fine.
The main goal of Computer Vision with Embedded Machine Learning courses.
The main goal of this section is to teach students about the different Machine Learning Methods and give them ideas on how they can apply them to their project. In addition, you are required to build a mobile app that would demonstrate your understanding of one or more of the topics touched upon in this Course and teach others about what you have learned as well!
Project: What you will build for this online courses app. Students in this course will develop a mobile app that demonstrates their understanding of one or more of the topics touched upon and teach others about what they have learned!
What are the main components, and what areas should you study?
This course will cover the following topics:
- Introduction to BioImages, Web Images, and Video.
This section will cover the different types of images that we work with, how these classes overlap, and how to tell them apart. We will talk about various features and geometrical features and explain what they mean and why these are important in image analysis.
- Introduction to Machine Learning, Machine Learning for Computer Vision.
This section will cover the different types of Machine Learning Methods and their relation to Computer Vision. We will also talk about Data Pre-Processing, Feature Selection, and Attribute Weighting.
- Real-Time & Embedded Machine Learning, Machine Learning for Embedded Systems.
This section will talk about Real-Time Machine Learning Methods and Embedded Machine Learning Methods. Then, we will describe how we use real-time methods and explain what these methods look like in real-life applications.
- Image processing and Optical Character Recognition (OCR).
This section will talk about Optical Character Recognition (OCR) and how we can perform OCR using Machine Learning. We will also discuss how Optical Character Recognition (OCR) and Machine Learning work together in real-life applications.
- Image Classification & Object Detection.
This section will talk about Image Classification and Object Detection for Image Processing with Computer Vision Applications. We will also demonstrate image classification methods using computer vision applications. One such example is object detection for image processing with computer vision applications, including video surveillance.
What did you like best about the Course?
This Course is excellent if you want to learn machine learning, computer vision, and embedded machine learning all at once! It was one of the best courses I took in Udemy; Im delighted that I took this class. The lectures can be lengthy, but it’s definitely worth the time. It took me about 1,5 months to complete the Course. It was a long process, but I enjoyed the entire process and learned a lot about Machine Learning and Computer Vision. The pace of the Course is perfect for learning about these topics. The instructor is also beneficial and will respond quickly to your questions. This course is perfect for learning Machine Learning, Computer Vision, and Embedded Machine Learning all at once! I highly recommend this course.
