The Ultimate Beginners Guide to Fuzzy Logic in Python

The Ultimate Beginners Guide to Fuzzy Logic in Python, available at $69.99, has an average rating of 4.8, with 37 lectures, based on 126 reviews, and has 1013 subscribers.

You will learn about 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 This course is ideal for individuals who are Anyone interested in fuzzy logic or Students who are taking courses on Artificial Intelligence or Data Science or Data Scientists who want to increase their knowledge in artificial intelligence algorithms It is particularly useful for Anyone interested in fuzzy logic or Students who are taking courses on Artificial Intelligence or Data Science or Data Scientists who want to increase their knowledge in artificial intelligence algorithms.

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Summary

Title: The Ultimate Beginners Guide to Fuzzy Logic in Python

Price: $69.99

Average Rating: 4.8

Number of Lectures: 37

Number of Published Lectures: 37

Number of Curriculum Items: 37

Number of Published Curriculum Objects: 37

Original Price: $19.99

Quality Status: approved

Status: Live

What You Will 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

Who Should Attend

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

Target Audiences

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

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!

Course Curriculum

Chapter 1: Introduction

Lecture 1: Course content

Lecture 2: Course materials

Chapter 2: Basic intuition

Lecture 1: Plan of attack

Lecture 2: Applications of fuzzy logic

Lecture 3: First understanding

Lecture 4: Linguistic variables and membership

Lecture 5: Steps for fuzzy inference

Lecture 6: Defuzzification – calculation

Chapter 3: Fuzzy control systems – implementation

Lecture 1: Plan of attack

Lecture 2: Tipping problem 1 – libraries

Lecture 3: Tipping problem 2 – antecedents and consequent

Lecture 4: Tipping problem 3 – membership functions

Lecture 5: Tipping problem 4 – rules

Lecture 6: Tipping problem 5 – defuzzification

Lecture 7: Fuzzy functions – sigmoid, gaussian and PI

Lecture 8: HOMEWORK – vacuum cleaner problem 1

Lecture 9: Vacuum cleaner problem 2

Lecture 10: Vacuum cleaner problem 3

Lecture 11: Tipping problem – hard fuzzy 1

Lecture 12: Tipping problem – hard fuzzy 2

Lecture 13: Tipping problem – hard fuzzy 3

Lecture 14: Tipping problem – hard fuzzy 4

Lecture 15: Tipping problem – hard fuzzy 5

Lecture 16: Tipping problem – hard fuzzy 6

Lecture 17: Tipping problem – hard fuzzy 7

Lecture 18: Tipping problem – hard fuzzy 8

Lecture 19: Other defuzzification methods

Lecture 20: HOMEWORK – vacuum cleaner hard fuzzy

Chapter 4: Clustering with fuzzy c-means

Lecture 1: Plan of attack

Lecture 2: Clustering – intuition

Lecture 3: Loading the dataset

Lecture 4: Preprocessing the dataset

Lecture 5: Clustering with fuzzy c-means

Lecture 6: Choosing the number of clusters

Lecture 7: Interpreting the results

Chapter 5: Final remarks

Lecture 1: Final remarks

Lecture 2: BONUS

Instructors

Jones Granatyr
Professor

Eduardo Alexandre Franciscon
Pesquisador

AI Expert Academy
Instructor

Rating Distribution

1 stars: 1 votes

2 stars: 4 votes

3 stars: 11 votes

4 stars: 37 votes

5 stars: 73 votes

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