6 Week Course – Three Classes Per Week
| Available Class Times |
|---|
|
Monday, Wednesday, Friday |
Course in Progress – Get notified about future classes
Upon successful completion of this course, students will receive a Certificate of Completion issued by Hanku and backed by our university partner, Florida State University’s Jim Moran College of Entrepreneurship.
This certificate may be added to your LinkedIn profile to showcase your participation, newly developed skills, and commitment to continued learning.
Anyone who:
- Wants to utilize AI in their daily work
- Wants to automate tasks and work more efficiently
- Is curious about AI but is unfamiliar with technical jargon
- Needs to speak confidently about AI in meetings and decisions
- Is a marketer, manager, consultant, entrepreneur, or business professional
- Wants a better understanding of AI in general
- Wants hands-on experience, not just theory
No coding experience required. No prerequisites.
This intensive online course hosted on Canvas provides students with a comprehensive introduction to artificial intelligence, covering fundamental concepts, algorithms, and real-world applications.
Students will explore the historical development of AI, understand core machine learning paradigms, and examine how AI systems solve complex problems across various domains.
The course emphasizes hands-on learning through interactive activities, algorithm implementation, and analysis of contemporary AI applications, including neural networks, computer vision, natural language processing, and robotics.
Students will develop both theoretical understanding and practical skills necessary to evaluate, design, and implement AI solutions.
- Analyze the foundations of artificial intelligence by comparing AI approaches with human intelligence and understanding historical developments in the field
- Classify and implement core AI algorithms including supervised learning, unsupervised learning, and reinforcement learning techniques
- Design and evaluate machine learning models using appropriate algorithms, data preprocessing, and validation methodologies
- Apply probability theory and statistical concepts to understand uncertainty, distribution modeling, and decision-making in AI systems
- Assess ethical implications and biases in AI systems and propose solutions for responsible AI development
- Develop practical AI applications across diverse domains including games, healthcare, finance, and creative industries
- Integrate hardware considerations into AI system design and understand computational requirements for different AI approaches
Upon successful completion of this course, students will:
- Differentiate between various AI paradigms including symbolic AI, connectionist approaches, and hybrid systems
- Implement fundamental machine learning algorithms including perceptrons, decision trees, and neural networks
- Analyze datasets using clustering, classification, and regression techniques with appropriate evaluation metrics
- Apply probability distributions and statistical methods to model uncertainty in AI decision-making processes
- Evaluate AI systems for bias, fairness, and ethical considerations in real-world deployment scenarios
- Design AI solutions for specific application domains including game-playing, image recognition, and natural language processing
- Assess computational requirements and hardware constraints for different AI algorithms and architectures
- Demonstrate proficiency in the complete machine learning pipeline from data collection through model deployment
- 1. Introduction to AI
- 2. AI vs Human Intelligence vs ML vs Pattern Recognition
- 3. Types of AI
- 4. Machine Learning Basics
- 5. Introduction to Probability
- 6. Data: Input/Output/Types
- 7. AI Hardware
- 8. Bias and Ethics in AI
- 9. Activity: Quiz on the Basics
- 10. Introduction to Algorithms
- 11. Input/Output/Run-Time
- 12. Supervised Learning
- 13. Unsupervised Learning
- 14. Reinforcement Learning
- 15. Perceptrons
- 16. Support Vector Machines (SVMs)
- 17. Decision Trees
- 18. Neural Networks
- 19. Test/Train/Validate Cycle
- 20. Activity: Algorithm Practice
- 21. AI in Games
- 22. 3D Modeling and Computer Graphics
- 23. Healthcare Applications
- 24. Art and Music Generation
- 25. Natural Language Processing
- 26. Robotics and Autonomous Systems
- 27. Finance and Trading
- 28. Final Activity: Chatbot Development
Contact Us for Assistance.
