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Artificial Intelligence and IoT: Naive Bayes

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Artificial Intelligence and IoT: Naive Bayes, available at $54.99, has an average rating of 4.35, with 40 lectures, 4 quizzes, based on 54 reviews, and has 7218 subscribers.

You will learn about 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 This course is ideal for individuals who are Anyone curious about AIoT or Anyone who wants to build AIoT systems or Anyone who wants to implement AI on microcontrollers or Anyone who wants to implement Naive Bayes classifier from scratch in Python and C or Anyone who wants to learn Naive Bayes classifier by hand It is particularly useful for Anyone curious about AIoT or Anyone who wants to build AIoT systems or Anyone who wants to implement AI on microcontrollers or Anyone who wants to implement Naive Bayes classifier from scratch in Python and C or Anyone who wants to learn Naive Bayes classifier by hand.

Enroll now: Artificial Intelligence and IoT: Naive Bayes

Summary

Title: Artificial Intelligence and IoT: Naive Bayes

Price: $54.99

Average Rating: 4.35

Number of Lectures: 40

Number of Quizzes: 4

Number of Published Lectures: 40

Number of Published Quizzes: 4

Number of Curriculum Items: 44

Number of Published Curriculum Objects: 44

Original Price: $199.99

Quality Status: approved

Status: Live

What You Will 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

Who Should Attend

  • 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

Target Audiences

  • 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

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.

Course Curriculum

Chapter 1: Introduction

Lecture 1: Prologue

Lecture 2: Required Components

Lecture 3: Source Code

Chapter 2: === Part 1: Theory

Lecture 1: Welcome to Part 1: Theory

Chapter 3: Naive Bayes Classifier by Hand (One Input Feature)

Lecture 1: What is Naive Bayes?

Lecture 2: Naive Bayes Example 1

Lecture 3: The Naive Bayes Formula

Lecture 4: Laplace Smoothing

Lecture 5: Summary

Chapter 4: Naive Bayes Classifier by Hand (Multiple Input Features)

Lecture 1: Naive Bayes Example 2

Lecture 2: The Naive Bayes Classifier Formula (Updated)

Lecture 3: Summary

Chapter 5: === Part 2: Modelling

Lecture 1: Welcome to Part 2: Modelling

Chapter 6: AI Light Bulb Modelling by Hand

Lecture 1: AI Light Bulb

Lecture 2: Training Data

Lecture 3: Build the Model

Chapter 7: AI Light Bulb Modelling in Python

Lecture 1: Required Programming Tools

Lecture 2: Data Preprocessing

Lecture 3: Create Frequency Tables

Lecture 4: The Solution to the Quiz (Create Frequency Tables)

Lecture 5: Calculate Prior Probability

Lecture 6: Calculate Likelihood

Lecture 7: Calculate Posterior Probability

Lecture 8: Summary

Chapter 8: AI Light Bulb Modelling in C

Lecture 1: Required Programming Tools

Lecture 2: Overview of Naive Bayes in C

Lecture 3: Naive Bayes Functions in C

Chapter 9: === Part 3: Prototyping

Lecture 1: Welcome to Part 3: Prototyping

Chapter 10: AI Light Bulb Prototyping in Arduino ESP32: Serial I/O, Sensors, LED

Lecture 1: Required Programming Tools

Lecture 2: AI Light Bulb with Serial Monitor I/O

Lecture 3: LED

Lecture 4: BH1750 Light Sensor

Lecture 5: DS1307 Real-Time Clock

Lecture 6: SSD1306 OLED Display

Lecture 7: AI Light Bulb with LED, Light Sensor, and Real-Time Clock

Chapter 11: AI Light Bulb Prototyping in Arduino ESP32: Wi-Fi, Telegram Bot

Lecture 1: Create a New Telegram Bot

Lecture 2: Wi-Fi and Telegram Bot

Lecture 3: Control an LED using Telegram Bot

Lecture 4: AI Light Bulb with Telegram Bot

Chapter 12: Summary

Lecture 1: Summary of the Course

Instructors

Rating Distribution

  • 1 stars: 1 votes
  • 2 stars: 2 votes
  • 3 stars: 7 votes
  • 4 stars: 22 votes
  • 5 stars: 22 votes

Frequently Asked Questions


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