What you would learn in The Ultimate Beginners Guide to Fuzzy Logic in Python course?
Fuzzy Logic can be described as a method that can be utilized to simulate the human thinking process on computers. It can be used in many areas, like manufacturing automation, medicine in the home, marketing, and many more. One of the most well-known examples is an application in industrial equipment equipped with temperature control that automatically adjusts when the machine is heated as it cools. Other examples of equipment include vacuum cleaners (adjustment of suction power by the surface and the amount of dirt), dishwashers and washers (adjustment in the volume of soap and water to use), Digital cameras (automatic fixation of the focus) as well as air conditioning (temperature setting based on the conditions) as well as microwave (power adjustment by the kind of food).
In this class, you'll be introduced to the fundamentals of fuzzy logic. It will also cover applying basic fuzzy systems with the skfuzzy library. The implementations will be completed step-by-step with this Python programming language! Below, you will find the entire material, which is split into three sections:
Part 1. The fundamental understanding of fuzzy logic. The topics covered include: the linguistic language, antecedents, and linguistic variables consequents, membership functions, fuzzification, mathematical calculations for defuzzification
Part 2 implementation of fuzzy systems. The examples you will use are such as the calculation of the tips to be offered in restaurants (based on the standard of food and the level of service) and also the calculation of the suction capacity of the vacuum cleaner (based on the type of surface used and how much dirt )
Third Part: Clustering using a fuzzy C-means algorithm. We will group the bank's customers according to their credit limit and total amount. It will be clear how fuzzy logic can be used in the field of Machine Learning. Machine Learning
The implementations will be completed step-by-step by using Google Colab online, so you won't have to worry about installing these libraries onto your personal computer. After that, you'll be able to create your projects with fuzzy logic!
- Learn the theories behind fuzzy logic. Examples include language variables, antecedents, and consequent members, fuzzification and defuzzification
- Learn how to calculate defuzzification using the following methods: centroid bisector MOM, SOM, LOM
- Implement fuzzy systems using skfuzzy library
- Create a fuzzy system to select the percentage of tips offered at a restaurant.
- Make a fuzzy-like system to modify the suction force of a vacuum cleaner by the kind of surface and quantity of dirt.
- Use data clustering to create clusters by using the fuzzy c-means algorithm.
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