Computer Vision on Raspberry Pi – Beginner to Advanced
Computer Vision on Raspberry Pi – Beginner to Advanced. Build Real-World Computer Vision Applications on Raspberry Pi and Learn Basics of Neural Networks using Google Colab.
Computer Vision on Raspberry Pi Course Content Overview
Udemy Computer Vision on Raspberry Pi – Beginner to Advanced Course
Computer Vision Applications on Raspberry Pi is a beginner course on the newly launched Raspberry Pi 4 and is fully compatible with Raspberry Pi 3/2 and Raspberry Pi Zero. The course is ideal for those new to the Raspberry Pi and who want to explore more about it.
You will learn the components of Raspberry Pi, connecting components to Raspberry Pi, installation of the NOOBS operating system, basic Linux commands, Python programming and building Image Processing applications on Raspberry Pi and the basics of neural networks.
This course will take beginners without coding skills to a level where they can write their own programs. The basics of Python programming language are well covered in the course. Building Computer Vision applications are taught in the simplest manner, which is easy to understand.
Users can quickly learn hardware assembly and coding in Python programming for building Computer Vision applications. By the end of this course, users will have enough knowledge about Raspberry Pi, its components, basic Python programming, and execution of Image Processing applications in real-time scenarios.
The course is taught by an expert team of engineers having PhD and Postdoctoral research experience in Computer Vision and Deep Learning. Anyone can take this course. No engineering knowledge is expected. The tutor has explained all required engineering concepts in the simplest manner. The course will enable you to independently build Computer Vision applications using Raspberry Pi. This course is the easiest way to learn and become familiar with the Raspberry Pi platform.
By the end of this course, users will build Image Processing applications which include scaling and flipping images, varying the brightness of images, performing bit-wise operations on images, blurring and sharpening images, thresholding, erosion and dilation, edge detection, and image segmentation. User will also be able to build real-world Image Processing applications, which includes real-time human face eyes nose detection, detecting cars in the video, real-time object detection, human face recognition, convolutional neural network and many more.
What you’ll learn
- What is Raspberry Pi? and what are its components?
- Understand peripherals that need to be connected to Raspberry Pi
- Wire up your Raspberry Pi to create a fully functional computer
- Easily learn preparing SD Card to load Operating System for Raspberry Pi
- Install packages needed to build Computer Vision applications
- Learn basic programming aspects of Python
- Create simple Image Processing applications using Python and OpenCV
- Build real-world Image Processing applications on Raspberry Pi 4/3/2/Zero
- Learn basics of Neural Network using Google Colab
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Course Information
- Instructor: Dr. Steven Fernandes, Ramesh Nayak
- Duration: 9 hours
- Language: English
- Source: Udemy
Courses Reviews
Instructor
Dr. Steven Fernandes
My name is Dr. Steven Fernandes, and I am super-delighted that you are reading this!
I have Postdoctoral research experience working in Deep Learning and Computer Vision at the University of Alabama Birmingham, USA, and the University of Central Florida, USA. I have a Bachelor’s, Master’s, and PhD degree in Electronics and Communication Engineering. I have 10 years of experience developing Computer Vision applications on Raspberry Pi. I am passionate about developing various innovative Computer Vision applications on Raspberry Pi and look forward to sharing my passion and knowledge with you!
Ramesh Nayak
Hi Welcome to the course. Myself, Ramesh Nayak.
I did Bachelor’s degree in Industrial Production and Master’s degree in Computer Science and Engineering. I have 19 years of teaching experience and helped many students to achieve success, both in academics and also in projects. I am currently pursuing PhD in image processing and very much interested to share the knowledge. You will definitely find this course easy and useful.
