The Ultimate Beginners Guide to Fuzzy Logic in Python

The Ultimate Beginners Guide to Fuzzy Logic in Python

Understand the basic theory and implement fuzzy systems with skfuzzy library! The Ultimate Beginners Guide to Fuzzy Logic in Python.

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

About The Course The Ultimate Beginners Guide to Fuzzy Logic in Python

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!

Online Course CoupoNED based Analytics Education Company and aims at Bringing Together the analytics companies and interested Learners.