OpenCourser brand icon
Sign in
We may earn an affiliate commission when you visit our partners.
Course image
Udemy logo

Artificial Intelligence and IoT

Naive Bayes

4.2 Filled star Filled star Filled star Filled star Empty star
Based on 55 ratings
see reviews
Erwin Setiawan

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.

Read more

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.

Enroll now

Here's a deal for you

Save money when you learn with a deal that may be relevant to this course.
All coupon codes, vouchers, and discounts are applied automatically unless otherwise noted.
Only 3 hours left!
24-Hour Flash Sale
Close your skills gaps with online courses and save big for a limited time.
Up to
85%
off
Get offer

What's inside

Learning objectives

  • 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

Syllabus

Introduction

Welcome to the course!

Required Components
Source Code
Read more

In this lecture you will learn:

  1. Naive Bayes

  2. Conditional probability

Enjoy AIoT!

In this lecture you will learn:

  1. Naive Bayes example (one input feature) by hand

  2. Step-by-step of Naive Bayes algorithm (one input feature)

Enjoy AIoT!

In this lecture you will learn:

  1. Naive Bayes formula (posterior probability, likelihood, and prior probability)

  2. Naive Bayes classifier formula (ignore the denominator of the Naive Bayes formula)

Enjoy AIoT!

In this lecture you will learn:

  1. Zero frequency

  2. Laplace smoothing

Enjoy AIoT!

Summary of Naive Bayes classifier

In this lecture you will learn:

  1. Naive Bayes example (multiple input features) by hand

  2. Step-by-step of Naive Bayes algorithm (multiple input features)

Enjoy AIoT!

In this lecture you will learn:

  1. Naive Bayes classifier formula (updated)

Enjoy AIoT!

AI light bulb introduction

In this lecture you will learn:

  1. Training data for AI light bulb

Enjoy AIoT!

In this lecture you will learn:

  1. How to model the AI light bulb by hand

  2. The step-by-step of Naive Bayes algorithm for AI light bulb

Enjoy AIoT!

In this lecture you will learn:

  1. Read the dataset from the CSV file

  2. Data preprocessing (features and label)

  3. Split the dataset into features and label

Enjoy AIoT!

In this lecture you will learn:

  1. Create the frequency table for light sensor

  2. Create the frequency table for time

Enjoy AIoT!

In this lecture you will learn:

  1. Create the prior probability function

Enjoy AIoT!

In this lecture you will learn:

  1. Create the likelihood function

Enjoy AIoT!

In this lecture you will learn:

  1. Create the posterior probability function

Enjoy AIoT!

Summary of AI light bulb modelling in Python

In this lecture you will learn:

  1. Generate C array containing features and label

  2. Overall C model of Naive Bayes

Enjoy AIoT!

In this lecture you will learn:

  1. Port Naive Bayes functions from Python model to C model

  2. Create a new function for predicting the output

Enjoy AIoT!

Build AI light bulb prototype that uses serial monitor as I/O interface

Introduction to LED

Introduction to BH1705 light sensor

Introduction to DS1307 real-time clock

Introduction to SSD1306 OLED display

Build AI light bulb prototype that employs sensors (light sensor, real-time clock) and actuators (LED and OLED display)

Introduction to Wi-Fi and Telegram bot

Integrate the LED with Wi-Fi and Telegram bot

Integrate the AI light bulb prototype with Wi-Fi and Telegram bot

Summary of the course

Traffic lights

Read about what's good
what should give you pause
and possible dealbreakers
Provides sample codes for every project, which allows learners to quickly grasp the practical aspects of building AIoT systems and apply the concepts to their own projects
Teaches implementation of Naive Bayes from scratch in both Python and C, which develops skills in different programming environments and allows for broader application in AIoT projects
Requires familiarity with programming tools, which may pose a barrier to entry for absolute beginners without prior experience in software development or embedded systems
Includes a section on prototyping with Arduino ESP32, covering serial I/O, sensors, LED, Wi-Fi, and Telegram bot integration, which provides practical experience in building a complete AIoT system
Covers building an AI light bulb prototype, which may not appeal to learners interested in other AIoT applications beyond smart home devices or those seeking broader applications of Naive Bayes
Requires specific components like Arduino ESP32, BH1705 light sensor, DS1307 real-time clock, and SSD1306 OLED display, which may require additional investment and effort to acquire

Save this course

Create your own learning path. Save this course to your list so you can find it easily later.
Save

Reviews summary

Build aiot system with naive bayes

According to learners, this course provides a positive, project-based approach to building an AIoT system using the Naive Bayes algorithm. Students appreciate the structure that moves from theory to implementation in Python and C, and finally to a physical prototype using Arduino ESP32. The focus on implementing algorithms from scratch is highlighted as a strength for understanding. However, some learners note that success in the course requires a solid foundation in programming (Python and C) and basic hardware concepts, suggesting it might be challenging for absolute beginners in these areas. The hardware setup and integration can also present a warning for those new to embedded systems.
Implementing algorithms from scratch aids understanding.
"Building Naive Bayes from scratch in Python helped me understand it deeply."
"The C implementation part was challenging but very rewarding for grasping the details."
"I appreciated the step-by-step process of coding the algorithm myself."
Unique blend of AI, IoT, and embedded systems.
"This course offers a unique perspective on applying AI to microcontrollers."
"It's rare to find a course that covers AI, Python, C, and Arduino together."
"Loved seeing how AI can run on resource-constrained devices."
Course effectively bridges theory, code, and hardware.
"It's great to see theory examples by hand, then coding, then hardware."
"The flow from concept to working prototype is well-structured."
"This course successfully combines AI, software, and embedded systems."
Practical project application is a key strength.
"I really liked how the course built up to a complete project."
"The AI light bulb prototype was a great way to see the concepts applied."
"This project-based structure made learning much more engaging for me."
Hardware setup and integration can be tricky.
"Getting the Arduino and sensors set up required some troubleshooting."
"Integrating the code with the physical components was the hardest part."
"Make sure you have the exact required hardware components ready."
Requires solid Python and C programming knowledge.
"Learners need a strong grasp of both Python and C before starting."
"The coding sections move quickly and assume prior experience."
"Not recommended if you are new to programming in C or Python."

Activities

Be better prepared before your course. Deepen your understanding during and after it. Supplement your coursework and achieve mastery of the topics covered in Artificial Intelligence and IoT: Naive Bayes with these activities:
Review Probability and Statistics Fundamentals
Reinforce your understanding of probability and statistics, which are essential for grasping the concepts behind Naive Bayes.
Browse courses on Conditional Probability
Show steps
  • Review basic probability concepts like conditional probability and Bayes' theorem.
  • Practice solving probability problems.
  • Familiarize yourself with common probability distributions.
Read 'Bayesian Reasoning and Machine Learning' by David Barber
Deepen your understanding of Bayesian methods by reading a book dedicated to Bayesian reasoning and machine learning.
View Bayesian Reasoning and Machine Learning... on Amazon
Show steps
  • Obtain a copy of 'Bayesian Reasoning and Machine Learning'.
  • Read the sections related to Naive Bayes classifiers.
  • Work through the examples and exercises provided in the book.
Naive Bayes Calculation Exercises
Solidify your understanding of Naive Bayes by working through calculation exercises with varying feature sets.
Show steps
  • Find or create datasets with different numbers of input features.
  • Calculate prior probabilities, likelihoods, and posterior probabilities by hand.
  • Compare your manual calculations with results from a Naive Bayes implementation.
Four other activities
Expand to see all activities and additional details
Show all seven activities
Read 'Pattern Recognition and Machine Learning' by Christopher Bishop
Expand your knowledge of machine learning by reading a chapter on Bayesian methods in a comprehensive machine learning textbook.
View Pattern Recognition and Machine Learning... on Amazon
Show steps
  • Obtain a copy of 'Pattern Recognition and Machine Learning'.
  • Read the chapter on probabilistic graphical models and Bayesian methods.
  • Take notes and summarize the key concepts.
Build a Simple Spam Filter
Apply your knowledge of Naive Bayes to a practical problem by building a spam filter using Python.
Show steps
  • Gather a dataset of spam and non-spam emails.
  • Preprocess the email text to extract features (e.g., word frequencies).
  • Implement a Naive Bayes classifier to classify emails as spam or not spam.
  • Evaluate the performance of your spam filter.
Create a Naive Bayes Explainer Video
Deepen your understanding by explaining Naive Bayes concepts in a video format, targeting an audience unfamiliar with the algorithm.
Show steps
  • Plan the content of your video, including key concepts and examples.
  • Create visuals to illustrate the Naive Bayes algorithm.
  • Record and edit your video, ensuring clarity and accuracy.
  • Share your video and gather feedback.
Contribute to an Open Source AIoT Project
Gain practical experience by contributing to an open-source project that utilizes AIoT and potentially Naive Bayes.
Show steps
  • Find an open-source AIoT project on platforms like GitHub.
  • Explore the project's codebase and documentation.
  • Identify areas where you can contribute, such as bug fixes, new features, or documentation improvements.
  • Submit your contributions and participate in code reviews.

Career center

Learners who complete Artificial Intelligence and IoT: Naive Bayes will develop knowledge and skills that may be useful to these careers:
AIoT Developer
An AIoT Developer specializes in the development of Artificial Intelligence of Things systems, and this course is a perfect fit. This role requires a strong understanding of both AI and IoT, and this course teaches the entire process of building a system using the Naive Bayes algorithm on an Arduino microcontroller. The course also helps this developer with practical skills in programming in Python and C, and includes the steps involved in building a prototype, from theory to execution. An AIoT Developer would also be interested in this course's teaching of how to integrate sensors and actuators.
See salaries and explore the career path for AIoT Developer
Machine Learning Engineer
A Machine Learning Engineer develops and implements machine learning models, and this course directly assists this role by focusing on the Naive Bayes algorithm. This role involves developing algorithms for pattern recognition and prediction. The practical experience of implementing Naive Bayes from scratch using Python and C as seen in the course helps build a strong understanding of how these algorithms work.. The course's hands-on approach provides a good foundation for the iterative process of building and testing models, a core competency for a Machine Learning Engineer. The work also includes deploying models, which the course aids through its emphasis on building an AIoT system using Arduino.
See salaries and explore the career path for Machine Learning Engineer
Artificial Intelligence Specialist
An Artificial Intelligence Specialist focuses on the design, development, and implementation of AI systems, and the course's focus on a practical AIoT project, using the Naive Bayes algorithm, directly relates to their work. An Artificial Intelligence Specialist must have solid algorithm knowledge, which this course helps with through its explanation of the Naive Bayes algorithm, and its implementation of the algorithm by hand, in python, and in C. The course provides a complete implementation from the ground up and this helps individuals understand the inner workings of a system, which helps in turn with debugging and optimizing an AI system.
See salaries and explore the career path for Artificial Intelligence Specialist
Embedded Systems Engineer
An Embedded Systems Engineer designs and develops software for embedded systems, and this course provides an opportunity to improve their skills on machine learning in this context. This role involves programming microcontrollers, and the course provides the chance to implement the Naive Bayes algorithm on an Arduino microcontroller. The course gives experience with integrating sensors and actuators using the Arduino platform, which are important for many embedded systems projects. This is an example of how an Embedded Systems Engineer might integrate AI into their work. The course also provides instruction on working with the C language, which is commonly used in embedded development.
See salaries and explore the career path for Embedded Systems Engineer
Robotics Engineer
A Robotics Engineer designs, builds, and tests robots; the course helps an engineer use machine learning in robotics. This role involves integrating hardware and software, and this course teaches how to use AI on a microcontroller. Robotics engineers are often involved in developing AI solutions for robots, and the course provides a practical understanding of how to model the Naive Bayes algorithm, and also how to implement it. The project-based approach in this course allows the Robotics Engineer to learn about the full lifecycle of developing a system that uses machine learning and hardware, while also working with embedded systems.
See salaries and explore the career path for Robotics Engineer
Firmware Engineer
A Firmware Engineer develops low-level software that controls hardware, and this course provides insight into integrating AI into that process. The course provides hands-on experience in implementing a Naive Bayes algorithm in C and deploying it on an Arduino microcontroller, which is directly relevant to the Firmware Engineer's work. The course also helps a Firmware Engineer learn about the overall process of deploying a system from end-to-end, beginning with theory and ending in a prototype. The skills in this course will provide a Firmware Engineer with the knowledge to begin using machine learning techniques in their work.
See salaries and explore the career path for Firmware Engineer
Data Scientist
A Data Scientist analyzes complex data to extract insights and develop data-driven solutions; this course may be useful for that skillset. The implementation of Naive Bayes in Python, a common tool in data science, helps the Data Scientist understand how this classification algorithm functions and how to implement it for solutions. Further, the course helps them think about the full lifecycle of a solution, as seen in the course's progression from theory, to modeling, to prototyping. This course helps to build a Data Scientist's knowledge in areas such as data preprocessing and model creation that are useful in their day to day work and projects.
See salaries and explore the career path for Data Scientist
Software Engineer
A Software Engineer develops and maintains software applications, and this course may be helpful in improving their skillset. This role will help them improve their capacity to create AI applications. The course provides practical experience in implementing algorithms in Python and C, which are common languages used by Software Engineers. By going through the process of building a system based on the Naive Bayes algorithm from scratch, this course provides an improved understanding of software development. The course may also be helpful to a Software Engineer who wishes to transition to AI.
See salaries and explore the career path for Software Engineer
Research Scientist
A Research Scientist conducts research in various fields, and the work in this course may be useful to them. The course material covers the Naive Bayes algorithm, going from a theoretical understanding of the algorithm to a final implementation. The course also emphasizes the importance of understanding how the algorithm works. While they may not use the Naive Bayes algorithm itself in their research, the course provides a good view of how to approach a research project and how to implement it in software. The comprehensive treatment of a single algorithm in this course could help them improve their work.
See salaries and explore the career path for Research Scientist
Data Analyst
A Data Analyst collects, processes, and performs statistical analysis on data, and this course may be useful for improving their understanding of statistical methods in machine learning. The course material covers the Naive Bayes algorithm and how to implement it by hand, which is useful for a Data Analyst who is interested in improving their knowledge of machine learning modeling. This is an example of a Data Analyst learning how to implement an algorithm. This course is an example of learning an algorithm from first principles, and may be helpful to them if they want to increase their knowledge of a common algorithm.
See salaries and explore the career path for Data Analyst
Solutions Architect
A Solutions Architect designs and oversees the implementation of technology solutions, and this course may be useful in helping them improve their knowledge of AI systems. The course provides a practical overview of creating an AIoT system using the Naive Bayes algorithm, and provides hands-on experience working with AI and embedded systems. The course takes the learner through the lifecycle of the system, from theory to implementation. The course can help a Solutions Architect gain a deeper understanding of the technical details behind AI and IoT which may be useful to them.
See salaries and explore the career path for Solutions Architect
Technology Consultant
A Technology Consultant advises organizations on how to use technology to meet their goals. This course provides valuable insight into how Artificial Intelligence can be deployed on embedded systems. The course's hands-on approach to teaching the Naive Bayes algorithm, along with its implementation in a real-world project, provides a good understanding of the potential and constraints of such systems. By completing this course, a Technology Consultant can learn about practical AI and gain a better understanding of how to help clients with projects concerning AI and interconnected smart devices.
See salaries and explore the career path for Technology Consultant
Hardware Engineer
A Hardware Engineer designs and develops physical components of computer systems, and this course may be useful to them. This course teaches how the Naive Bayes algorithm, a common technique in AI, can be implemented on an embedded system. A Hardware Engineer that has knowledge of how AI algorithms are deployed on hardware may be a useful skill on a team that uses machine learning. The course also covers the implementation of machine learning on an Arduino platform. This course can help a Hardware Engineer explore the intersection of hardware and machine learning.
See salaries and explore the career path for Hardware Engineer
Quantitative Analyst
A Quantitative Analyst develops mathematical models for financial markets, and this course may be useful. This role sometimes calls for knowledge of machine learning techniques, such as the Naive Bayes algorithm presented in this course. This course can help a Quantitative Analyst understand the theory and implementation of the Naive Bayes algorithm, which is a classification algorithm. The course also helps build experience with algorithm implementation in Python. The skills acquired in this course would complement a Quantitative Analyst's work, particularly for classification tasks.
See salaries and explore the career path for Quantitative Analyst
Technical Writer
A Technical Writer creates documentation for technical products, and this course may be useful in understanding AI systems. This role requires a solid understanding of the systems that are being documented. The course teaches how AI algorithms can be deployed using an embedded system. By learning the process of developing a system, the Technical Writer can gain a deeper understanding of the concepts, which in turn will improve their ability to document technical products, especially those pertaining to AI and embedded systems.
See salaries and explore the career path for Technical Writer

Reading list

We've selected two books that we think will supplement your learning. Use these to develop background knowledge, enrich your coursework, and gain a deeper understanding of the topics covered in Artificial Intelligence and IoT: Naive Bayes.
Bayesian Reasoning and Machine Learning South Asian...
Save
Provides a thorough introduction to Bayesian methods in machine learning, including a detailed explanation of Naive Bayes classifiers. It covers the theoretical foundations and practical applications of Bayesian techniques. It is particularly useful for understanding the underlying assumptions and limitations of Naive Bayes. This book valuable reference for students and practitioners alike.
Bayesian Reasoning and Machine Learning Paperback
Paperback
$$$
Pattern Recognition and Machine Learning
Save
Provides a comprehensive overview of pattern recognition and machine learning techniques, including a detailed chapter on probabilistic graphical models and Bayesian methods. It offers a strong theoretical foundation for understanding Naive Bayes and its relationship to other machine learning algorithms. While not solely focused on Naive Bayes, it provides valuable context and advanced insights. This book is commonly used as a textbook in machine learning courses.
Pattern Recognition and Machine Learning...
Hardcover
$$$
Pattern Recognition and Machine Learning...
Paperback
$$$
Pattern Recognition and Machine Learning (text...
Unknown Binding
$$$$

Share

Help others find this course page by sharing it with your friends and followers:
Facebook
LinkedIn
Reddit
X
Link
Email

Similar courses

Here are nine courses similar to Artificial Intelligence and IoT: Naive Bayes.
Accelerate Migration from Control-M to Apache Airflow...
Most relevant
GenAI for DevOps Practitioners
Most relevant
Data Balancing with Gen AI: Credit Card Fraud Detection
Most relevant
Meta Marketing Science Certification Exam
Most relevant
Optical Character Recognition (OCR) with Document AI...
Most relevant
Programmatic Prompting with OpenAI: Refining and Filtering
Most relevant
Prerequisites and Advanced Machine Learning for NLP
Most relevant
NLP – Machine Learning Models in Python
Most relevant
PyMongo - Advanced
Most relevant

Udemy Buyer Protection & Guarantee

Verified partner policies & purchase protections

Global Marketplace Leader
Free Trial & Audit

Free Lecture Previews

Free preview video lectures available for all courses; optional 7-day free trial on Personal Plan subscriptions (cancel before trial ends to avoid conversion).

Return & Refund Policy

30-Day Money-Back Guarantee

30-day money-back guarantee on all eligible individual course purchases (provided course content has not been substantially consumed or downloaded). Subscription fees are non-refundable once billed.

Buyer Protections

Verified Academic & Industry Partner

Lifetime access to purchased individual courses and future updates, verifiable certificate of completion, and direct student Q&A forums.

Key Purchase Protections
30-day 100% money-back guarantee on course purchases
Lifetime access on web, mobile, and offline
Certificate of completion included with purchase
Direct instructor Q&A and active student community
OpenCourser Verified Partner Policy details last reviewed: 2026-10-22
Course image
Rating
4.2 Filled star Filled star Filled star Filled star Empty star
Effort
1.5 total hours
Via
Udemy
Instructor
Erwin Setiawan
Language
English
Traffic lights
Read about what's good
what should give you pause
and possible dealbreakers
Provides sample codes for every project, which allows learners to quickly grasp the practical aspects of building AIoT systems and apply the concepts to their own projects
Teaches implementation of Naive Bayes from scratch in both Python and C, which develops skills in different programming environments and allows for broader application in AIoT projects
Requires familiarity with programming tools, which may pose a barrier to entry for absolute beginners without prior experience in software development or embedded systems
Includes a section on prototyping with Arduino ESP32, covering serial I/O, sensors, LED, Wi-Fi, and Telegram bot integration, which provides practical experience in building a complete AIoT system
Covers building an AI light bulb prototype, which may not appeal to learners interested in other AIoT applications beyond smart home devices or those seeking broader applications of Naive Bayes
Requires specific components like Arduino ESP32, BH1705 light sensor, DS1307 real-time clock, and SSD1306 OLED display, which may require additional investment and effort to acquire
Share this
Share to help others discover this course.
Facebook LinkedIn X Reddit Link Email
Begin learning today
Enroll now to gain full access to Artificial Intelligence and IoT.
Enroll now
Save for later
Add this course to your list. Find it anytime.
Save
Our mission

OpenCourser helps millions of learners each year. People visit us to learn workspace skills, ace their exams, and nurture their curiosity.

Our extensive catalog contains over 50,000 courses and twice as many books. Browse by search, by topic, or even by career interests. We'll match you to the right resources quickly.

Find this site helpful? Tell a friend about us.

Affiliate disclosure

We're supported by our community of learners. When you purchase or subscribe to courses and programs or purchase books, we may earn a commission from our partners.

Your purchases help us maintain our catalog and keep our servers humming without ads.

Thank you for supporting OpenCourser.

© 2016 - 2025 OpenCourser