Artificial Intelligence Professional Course

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Enrolled: 34 students
Duration: 80 hours
Lectures: 12
Level: Beginner


Working hours

Monday 9:30 am - 6.00 pm
Tuesday 9:30 am - 6.00 pm
Wednesday 9:30 am - 6.00 pm
Thursday 9:30 am - 6.00 pm
Friday 9:30 am - 5.00 pm
Saturday Closed
Sunday Closed

The Short Course in Artificial Intelligence (AI) short course will provide learners with the foundation and branches of Artificial Intelligence, such as search strategies, knowledge representation and reasoning techniques. This course will also introduce a range of well-known techniques and applications in artificial intelligence, such as fuzzy logic machine learning, expert systems, natural language processing, and intelligent agents.

Learning Outcomes

  • Understand the importance of AI and its applications.
  • Apply a range of well-established AI search strategies and knowledge representation techniques in problem solving.
  • Assess a range of well-established techniques for reasoning with uncertain knowledge.
  • Understand a range of machine learning techniques.
  • Implement and evaluate a range of AI models and techniques for solving real-world problems.

Prerequisites and Target Audience

What will students need to know or do before starting this course?

  • This course is structured in a way that it is largely complete in itself.
  • Helpful but not required is familiarity with
    • Linear Algebra, Probability, Statistics
    • Computer Programming, preferably Python

Who should take this course? Who should not?

  • Executives and decision makers looking for a comprehensive overview of the subject
  • Professionals and students with a formal college education in Engineering, science, or mathematics

Topics Covered

Introduction to AI
Problem Solving Using Search
Knowledge Representation
Uncertain Knowledge
Fuzzy Logic
Machine Learning
Neural Network
Decision Tree
Genetic Algorithms
Experts Systems
Natural Language Processing
Intelligent Agents
AI Architect Business Intelligence Developer Big Data Engineer Data Scientist Machine Learning Engineer
As a minimum, participants should have: Good IT knowledge related to subject area Good understanding of English Knowledge of computer science, programming and mathematics/statistics.

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