AIoT Project: Naive Bayes based Smart Lighting

Name: AIoT Project: Naive Bayes based Smart Lighting
Rating: 4.2 (59 reviews)

A project-based course to build an AIoT system from theory to prototype

Created byErwin Ouyang

Last updated 5/2025

English

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

Course content

12 sections • 40 lectures • 1h 43m total length

Prologue1:49
Welcome to the course!
Required Components0:10
Source Code0:02

What is Naive Bayes?2:07
In this lecture you will learn:
Naive Bayes
Conditional probability
Enjoy AIoT!
Conditional Probability
Naive Bayes Example 17:29
In this lecture you will learn:
Naive Bayes example (one input feature) by hand
Step-by-step of Naive Bayes algorithm (one input feature)
Enjoy AIoT!
Naive Bayes with One Input Feature (by Hand)
The Naive Bayes Formula1:34
In this lecture you will learn:
Naive Bayes formula (posterior probability, likelihood, and prior probability)
Naive Bayes classifier formula (ignore the denominator of the Naive Bayes formula)
Enjoy AIoT!
Laplace Smoothing2:14
In this lecture you will learn:
Zero frequency
Laplace smoothing
Enjoy AIoT!
Summary0:29
Summary of Naive Bayes classifier

Naive Bayes Example 29:37
In this lecture you will learn:
Naive Bayes example (multiple input features) by hand
Step-by-step of Naive Bayes algorithm (multiple input features)
Enjoy AIoT!
Naive Bayes with Multiple Input Feature (by Hand)
The Naive Bayes Classifier Formula (Updated)1:13
In this lecture you will learn:
Naive Bayes classifier formula (updated)
Enjoy AIoT!
Summary0:49
Summary of Naive Bayes classifier

Required Programming Tools0:07
Data Preprocessing5:15
In this lecture you will learn:
Read the dataset from the CSV file
Data preprocessing (features and label)
Split the dataset into features and label
Enjoy AIoT!
Create Frequency Tables2:00
In this lecture you will learn:
Create the frequency table for light sensor
Create the frequency table for time
Enjoy AIoT!
The Solution to the Quiz (Create Frequency Tables)0:08
Calculate Prior Probability1:33
In this lecture you will learn:
Create the prior probability function
Enjoy AIoT!
Calculate Likelihood1:37
In this lecture you will learn:
Create the likelihood function
Enjoy AIoT!
Calculate Posterior Probability1:28
In this lecture you will learn:
Create the posterior probability function
Enjoy AIoT!
Summary1:18
Summary of AI light bulb modelling in Python

Required Programming Tools0:10
AI Light Bulb with Serial Monitor I/O5:35
Build AI light bulb prototype that uses serial monitor as I/O interface
LED3:10
Introduction to LED
BH1750 Light Sensor3:38
Introduction to BH1705 light sensor
DS1307 Real-Time Clock3:18
Introduction to DS1307 real-time clock
SSD1306 OLED Display3:34
Introduction to SSD1306 OLED display
AI Light Bulb with LED, Light Sensor, and Real-Time Clock5:59
Build AI light bulb prototype that employs sensors (light sensor, real-time clock) and actuators (LED and OLED display)

Requirements

Basic knowledge of Artificial Intelligence and IoT
Having some background in electronics and programming

Description

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

AIoT Project: Naive Bayes based Smart Lighting

What you'll learn

Explore related topics

Course content

Introduction3 lectures • 2min

=== Part 1: Theory1 lecture • 1min

Naive Bayes Classifier by Hand (One Input Feature)5 lectures • 14min

Naive Bayes Classifier by Hand (Multiple Input Features)3 lectures • 12min

=== Part 2: Modelling1 lecture • 1min

AI Light Bulb Modelling by Hand3 lectures • 8min

AI Light Bulb Modelling in Python8 lectures • 13min

AI Light Bulb Modelling in C3 lectures • 8min

=== Part 3: Prototyping1 lecture • 1min

AI Light Bulb Prototyping in Arduino ESP32: Serial I/O, Sensors, LED7 lectures • 25min

Requirements

Description

Who this course is for: