Foundations of A.I.- Knowledge Representation Learning

Foundations of A.I.: Knowledge Representation & Learning, available at $19.99, has an average rating of 4.1, with 24 lectures, 3 quizzes, based on 12 reviews, and has 43 subscribers.

You will learn about To study the principles of Artificial Intelligence To have deeper knowledge on various paradigms of Artificial Intelligence To provide the knowledge about knowledge representation and reasoning To understand the process of representing knowledge graphically To have adequate knowledge in developing expert systems This course is ideal for individuals who are Anyone interested in the field of Artificial Intelligence It is particularly useful for Anyone interested in the field of Artificial Intelligence.

Enroll now: Foundations of A.I.: Knowledge Representation & Learning

Summary

Title: Foundations of A.I.: Knowledge Representation & Learning

Price: $19.99

Average Rating: 4.1

Number of Lectures: 24

Number of Quizzes: 3

Number of Published Lectures: 24

Number of Published Quizzes: 3

Number of Curriculum Items: 27

Number of Published Curriculum Objects: 27

Original Price: ?2,799

Quality Status: approved

Status: Live

What You Will Learn

To study the principles of Artificial Intelligence

To have deeper knowledge on various paradigms of Artificial Intelligence

To provide the knowledge about knowledge representation and reasoning

To understand the process of representing knowledge graphically

To have adequate knowledge in developing expert systems

Who Should Attend

Anyone interested in the field of Artificial Intelligence

Target Audiences

Anyone interested in the field of Artificial Intelligence

In this course, we try to establish an understanding of how can computers or machines represent this knowledge and how can they perform inference. Representing information in the form of graphs, pictures and inferring information from pictures has been there since the inception of mankind. In this course, we look into few graphical methods of representing knowledge. In the second half of the course, we look into the learning paradigm. Learning or gaining information, processing information and reasoning are key concepts of Artificial Intelligence. In this course we look into the fundamentals of Machine Learning and methods that generalize knowledge. During this part of the journey, we will try to understand more about learning agent and how is it different from the other artificial intelligence agents. We will work on decision trees and simple linear regression as a part of machine learning in this course.

Intelligence is a very complex element in Humans which drives our lives. Take a decision or hire a candidate or solve a problem, intelligence is the key contributor. Since the bronze age, we tried to understand the evolution of intelligence and what are the key aspects that promote intelligence. One key element in promoting intelligence is representing knowledge we have acquired and inferring from the existing knowledge or deduction.

Course Curriculum

Chapter 1: About the Program

Lecture 1: Course Introduction

Lecture 2: Course Outline

Chapter 2: What is Artificial Intelligence

Lecture 1: What is A.I.?

Lecture 2: A.I. Paradigms

Lecture 3: Applications of A.I.

Chapter 3: Software Installation

Lecture 1: Installing Anaconda Distribution

Lecture 2: Handling Jupyter Notebooks 1

Lecture 3: Handling Jupyter Notebooks 2

Chapter 4: Knowledge Representation

Lecture 1: Knowledge based Agents

Lecture 2: Representing knowledge

Lecture 3: Knowledge Representation Techniques

Lecture 4: Expert Systems

Lecture 5: Build Expert System in Python

Lecture 6: Semantic Networks

Lecture 7: Building a Semantic Network using Networkx

Chapter 5: Learning

Lecture 1: Introduction to Machine Learning

Lecture 2: Types of Machine Learning

Lecture 3: Decision Trees

Lecture 4: Decision Trees with Python

Lecture 5: Applications of Decision Trees

Lecture 6: Linear Regression

Lecture 7: Linear Regression in Python

Lecture 8: Applications of Linear Regression

Chapter 6: About the Program

Lecture 1: Course Conclusion

Instructors

Prag Robotics
Robotics & A.I.

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4 stars: 6 votes

5 stars: 2 votes

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