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
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Prag Robotics
Robotics & A.I.
Rating Distribution
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- 2 stars: 0 votes
- 3 stars: 4 votes
- 4 stars: 6 votes
- 5 stars: 2 votes
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