AI Programming

Principles and Algorithms with Practical Application

4.9(72)12h 35m59 lectures6 sections

What you'll learn

Understand the basic concepts of AI
Learn to develop intelligent agents
Analyze data using advanced techniques
Design AI models effectively
Apply code and algorithms in practical projects
Use tools like Python and Anaconda

About this course

Start your journey in learning AI application programming through this comprehensive course. The course covers all fundamental and advanced aspects, from theoretical concepts to practical applications using modern tools and techniques. Learn how to design AI algorithms, analyze data, and develop practical projects that turn your ideas into reality.

Expected outcomes

Master the design and implementation of AI applications.
Understand the inner workings of AI algorithms.
Ability to analyze data and use it to improve processes.
Develop real projects using advanced programming tools.
Prepare to enter the professional field of AI applications.

Instructor

Eng. Amr Abdel Fattah

Computer systems engineer specializing in mobile app and website development, with experience in creating popular platforms and web applications.

3,186 students20 courses

AI Programming

Principles and Algorithms with Practical Application

4.9(72)12h 35m59 lectures6 sections

What you'll learn

Understand the basic concepts of AI
Learn to develop intelligent agents
Analyze data using advanced techniques
Design AI models effectively
Apply code and algorithms in practical projects
Use tools like Python and Anaconda

About this course

Course content

Introduction to Artificial Intelligence

3 lectures

Intelligent Agents

8 lectures

Introduction to Smart Agent1:36
What is an Agent15:34
Rational Agent8:11
PEAS Analysis for Agent8:56
Types of Environments9:48
Types of Agents (Part One)12:08
Types of Agents (Part Two)23:26
Types of Agents (Part Three)17:15

Search Operations

15 lectures

Introduction to the Research Process8:57
Steps to Convert the Problem9:05
Practical Example of Problem Transformation Steps12:23
Determining the Number of Cases20:35
Organizing Cases18:01
Search Method within the Search Tree18:40
First Search Strategy DFS23:51
Second Strategy BFS14:07
Practical Application of the First and Second Strategies DFS & BFS17:17
Third Strategy ID12:43
Fourth Strategy UCS16:51
Fifth Strategy GBFS25:15
Sixth Strategy A Star21:33
Seventh Strategy Minimax33:43
Practical Application Maze

Knowledge

10 lectures

Introduction to Knowledge2:00
Logical Thinking6:12
Default Logic (Part One)14:41
Default Logic (Part Two)9:31
Model Checking Algorithm14:38
Practical Example of Model Checking Algorithm25:04
Practical Application Model Checking
Knowledge Engineering29:25
The Code Used in Designing the Clue Game
First-Order Logic17:18

Probability

12 lectures

Introduction to Probability2:44
Probability and Unconditional Probability12:17
Conditional Probability10:29
Random Variable and Independence19:00
Bayes' Theorem12:01
Joint Probability11:12
Probability Rules20:46
Bayesian Network25:31
Inference for Probability11:55
Practical Application of Building a Bayesian Network25:23
Downloading the Application and Codes
Installing Anaconda2:09

Optimization

11 lectures

Introduction to Optimization1:50
Local Search7:31
Hill Climbing Algorithm12:07
Algorithm Variables8:39
Practical Application24:10
Downloading the App and Codes
Annealing Simulation Algorithm16:39
Linear Programming Algorithm16:40
Practical Solution to Linear Programming Algorithm Problem10:02
Downloading the App and Codes
Machine Learning6:27

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