The Ultimate Beginners Guide to Fuzzy Logic in Python

Name: The Ultimate Beginners Guide to Fuzzy Logic in Python
Rating: 4.2 (203 reviews)

Understand the basic theory and implement fuzzy systems with skfuzzy library! Step by step implementations

Created byJones Granatyr, Eduardo Alexandre Franciscon, AI Expert Academy

Last updated 10/2025

English

What you'll learn

Understand the theoretical concepts of fuzzy logic, such as: linguistic variables, antecedents, consequent, membership, fuzzification, and defuzzification
Learn defuzzification calculations using the following methods: centroid, bisector, MOM, SOM and LOM
Implement fuzzy systems using skfuzzy library
Simulate a fuzzy system to choose the percentage of tip that would be given in a restaurant
Simulate a fuzzy system to adjust the suction power of a vacuum cleaner, according to the type of surface and amount of dirt
Implement data clustering using the fuzzy c-means algorithm

Course content

5 sections • 37 lectures • 4h 40m total length

Course content6:13
Course materials0:09

Plan of attack2:22
Applications of fuzzy logic3:55
Explore how fuzzy logic applies to home and industrial devices, from washing machines and air conditioners to vacuum cleaners, dishwashers, microwaves, and cameras, optimizing water, power, and detergent.
First understanding11:22
Understand the basics of fuzzy logic by modeling cost and benefit with multi-valued rules, partial truth, and shapes like triangle, trapezoid, shoulder trapezoid, and sigmoid.
Linguistic variables and membership9:17
Explore the history of fuzzy logic and understand degrees of membership and linguistic variables. Learn how membership functions quantify category belonging using temperature examples.
Steps for fuzzy inference5:42
Apply fuzzy inference steps in Python to predict restaurant tips by defining antecedents for food and service quality, setting an output tip with linguistic categories, and building rules and falsification.
Defuzzification - calculation9:31

Plan of attack2:25
Implement practical fuzzy logic in Python using a library with pre-built functions. Compare easy and hard approaches on two samples: typing quality and service, and vacuum suction control.
Tipping problem 1 - libraries6:45
Explore building a fuzzy control system for tipping, modeling service and food quality as inputs to predict tip, using Google Colab and the SickKids Fuzzy library.
Tipping problem 2 - antecedents and consequent4:49
Define the antecedents for the foods quality and service and the consequence for tip, with input ranges 0–10 and output range 0–20, and prepare for membership function generation.
Tipping problem 3 - membership functions5:46
Tipping problem 4 - rules12:47
Tipping problem 5 - defuzzification8:32
Fuzzy functions - sigmoid, gaussian and PI11:02
HOMEWORK - vacuum cleaner problem 15:05
Vacuum cleaner problem 210:01
Vacuum cleaner problem 38:18
Tipping problem - hard fuzzy 15:35
Tipping problem - hard fuzzy 215:28
Tipping problem - hard fuzzy 33:35
Tipping problem - hard fuzzy 45:06
Tipping problem - hard fuzzy 513:37
Tipping problem - hard fuzzy 614:34
Compute tipping rules with fuzzy logic in Python using max, min, and mean functions to map food and service quality to low, median, and high tips.
Tipping problem - hard fuzzy 78:33
Tipping problem - hard fuzzy 810:47
Other defuzzification methods7:59
HOMEWORK - vacuum cleaner hard fuzzy10:26

Requirements

Basic Python programming

Description

Fuzzy Logic is a technique that can be used to model the human reasoning process in computers. It can be applied to several areas, such as: industrial automation, medicine, marketing, home automation, among others. A classic example is the use in industrial equipments, which can have the temperature automatically adjusted as the equipment heats up or cools down. Other examples of equipments are: vacuum cleaners (adjustment of suction power according to the surface and level of dirt), dishwashers and clothes washing machines (adjustment of the amount of water and soap to use), digital cameras (automatic focus setting), air conditioning (temperature setting according to the environment), and microwave (power adjustment according to the type of food).

In this course, you will learn the basic theory of fuzzy logic and mainly the implementation of simple fuzzy systems using skfuzzy library. All implementations will be done step by step using the Python programming language! Below you can see the main content, which is divided into three parts:

Part 1: Basic intuition about fuzzy logic. You will learn topics such as: linguistic variables, antecedents, consequent, membership functions, fuzzification and mathematical calculations for defuzzification
Part 2: Implementation of fuzzy systems. You will implement two examples: the calculation of tips that would be given in a restaurant (based on the quality of the food and the quality of service) and the calculation of the suction power of a vacuum cleaner (based on the type of surface and the amount of dirt )
Part 3: Clustering with fuzzy c-means algorithm. We will cluster a bank's customers based on the credit card limit and the total bill. You will understand how fuzzy logic can be applied in the area of Machine Learning

All implementations will be done step by step using Google Colab on-line, so you don't need to worry about installing the libraries on your own machine. At the end, you will be able to create your own projects using fuzzy logic!

Who this course is for:

Anyone interested in fuzzy logic
Students who are taking courses on Artificial Intelligence or Data Science
Data Scientists who want to increase their knowledge in artificial intelligence algorithms

The Ultimate Beginners Guide to Fuzzy Logic in Python

What you'll learn

Explore related topics

Course content

Introduction2 lectures • 6min

Basic intuition6 lectures • 42min

Fuzzy control systems - implementation20 lectures • 2hr 51min

Clustering with fuzzy c-means7 lectures • 58min

Final remarks2 lectures • 3min

Requirements

Description

Who this course is for: