Artificial Intelligence-Computational Intelligence in Python

Artificial Intelligence-Computational Intelligence in Python, available at $49.99, has an average rating of 3.9, with 33 lectures, based on 24 reviews, and has 124 subscribers.

You will learn about Students will learn the basic techniques of Computational Intelligence/ Soft computing including Fuzzy Logic Systems, Genetic Algorithm, Artificial Neural networks and Hybrid Intelligent Systems like ANFIS. This course is designed to explain these complex concepts of soft computing in an easy to understand and simplified manner with practical examples implemented in python. This course is ideal for individuals who are This course is highly recommended for anyone interested in computational intelligence/soft computing. It will also be very helpful for students/data analyst looking for explaining AI models (XAI). It is particularly useful for This course is highly recommended for anyone interested in computational intelligence/soft computing. It will also be very helpful for students/data analyst looking for explaining AI models (XAI).

Enroll now: Artificial Intelligence-Computational Intelligence in Python

Summary

Title: Artificial Intelligence-Computational Intelligence in Python

Price: $49.99

Average Rating: 3.9

Number of Lectures: 33

Number of Published Lectures: 33

Number of Curriculum Items: 33

Number of Published Curriculum Objects: 33

Original Price: $24.99

Quality Status: approved

Status: Live

What You Will Learn

Students will learn the basic techniques of Computational Intelligence/ Soft computing including Fuzzy Logic Systems, Genetic Algorithm, Artificial Neural networks and Hybrid Intelligent Systems like ANFIS. This course is designed to explain these complex concepts of soft computing in an easy to understand and simplified manner with practical examples implemented in python.

Who Should Attend

This course is highly recommended for anyone interested in computational intelligence/soft computing. It will also be very helpful for students/data analyst looking for explaining AI models (XAI).

Target Audiences

This course is highly recommended for anyone interested in computational intelligence/soft computing. It will also be very helpful for students/data analyst looking for explaining AI models (XAI).

This course covers the fundamentals of Computational Intelligence/ Soft computing including Fuzzy Logic Systems, Genetic Algorithm, Artificial Neural networks, and Hybrid Intelligent Systems like Adaptive Neuro-Fuzzy Inference System (ANFIS). It is easy to understand for anyone interested in the field of Computational Intelligence and includes practical examples of the techniques implemented in Python.

Course Curriculum

Chapter 1: Introduction

Lecture 1: Introduction

Lecture 2: Introduction to computational intelligence

Lecture 3: Installation of Tools

Chapter 2: Fuzzy Logic Systems

Lecture 1: Introduction to Fuzzy Logic System (FLS)

Lecture 2: Fuzzy Inference System (FIS)

Lecture 3: Example of a solving a problem with FLS

Lecture 4: Problem statement

Lecture 5: Fuzzy Rules

Lecture 6: Defuzzification

Lecture 7: Crisp Output

Chapter 3: Evolutionary Computation

Lecture 1: Introduction to Evolutionary Algorithms (EA)

Lecture 2: Introduction to Genetic Algorithm (GA)

Lecture 3: Fitness Function

Lecture 4: Initialize population

Lecture 5: Define Parameters of GA

Lecture 6: Create GA constructor

Lecture 7: Optimal Solution from GA

Chapter 4: Artificial Neural Network (ANN)

Lecture 1: Introduction to Machine Learning

Lecture 2: Introduction to Artificial Neural Network (ANN)

Lecture 3: Define Problem Statement

Lecture 4: Creating the ANN class

Lecture 5: Forward and Backward Pass

Lecture 6: Prediction Method

Lecture 7: Prediction Accuracy

Chapter 5: Adaptive Neuro Fuzzy Inference System (ANFIS)

Lecture 1: Introduction to Hybrid Intelligent System

Lecture 2: Introduction to ANFIS

Lecture 3: Description of Dataset used

Lecture 4: Import libraries and read the dataset

Lecture 5: Define Membership Functions

Lecture 6: Hybrid Training (LSE and BPA)

Lecture 7: Results with 2 Inputs

Lecture 8: Enhanced Accuracy with 4 Inputs

Lecture 9: Additional Reading Material

Instructors

Dr. Manish Kakar
Engineer and Researcher

Rating Distribution

1 stars: 0 votes
2 stars: 1 votes
3 stars: 6 votes
4 stars: 11 votes
5 stars: 6 votes

Frequently Asked Questions

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