Artificial Intelligence and IoT: Naive Bayes
What you'll learn
- Naive Bayes classifier examples by hand
- Implement Naive Bayes classifier from scratch in Python and C
- Implement Naive Bayes classifier on microcontrollers
- Build an AIoT system based on Naive Bayes classifier and Arduino
- Basic knowledge of Artificial Intelligence and IoT
- Having some background in electronics and programming
Sample codes are provided for every project in this course.
You will receive a certificate of completion when finishing this course.
There is also Udemy 30 Day Money Back Guarantee, if you are not satisfied with this course.
This course teaches you how to build an AIoT system from theory to prototype particularly using Naive Bayes algorithm. This course is divided into three main parts. In the first part, you will learn about Naive Bayes classifier examples by hand. In the second part, you will learn about how to implement Naive Bayes classifier from scratch in Python and C. In the third part, you will learn about how to build an AIoT system based on Naive Bayes classifier and Arduino.
This is a project-based course. The main goal is to show you the complete flow how to build AIoT from theory to prototype. The point is to apply the concepts that you will learn in this course to your own projects. At the end of this course, you will be able to combine various kinds of knowledge that you may have studied at university, such as Artificial Intelligence, Programming, and Embedded System, in order to build the complete prototypes.
So, click the course button and see you inside the course.
Who this course is for:
- Anyone curious about AIoT
- Anyone who wants to build AIoT systems
- Anyone who wants to implement AI on microcontrollers
- Anyone who wants to implement Naive Bayes classifier from scratch in Python and C
- Anyone who wants to learn Naive Bayes classifier by hand
He is an enthusiast in Microcontroller and FPGA with over 8000+ students on Udemy.
He worked as an R&D Engineer. He developed firmware and software for digital nurse call system and door access control system with AVR ATmega and .NET framework. He developed software for interfacing between medical laboratory equipment and laboratory information system based on RS-232 and TCP/IP.
He received his MSc degree in Electrical Engineering in 2018 from ITB. He is currently a researcher at Microelectronics Center of ITB. His research interests are in IoT, wireless communication, blockchain, and machine learning.
He writes articles and tutorials in his blogs: klinikarduino[dot]github[dot]io.