Description
In this course, you will learn:
- graph search algorithms
- adversarial search
- knowledge representation
- logical inference
- probability theory
- Bayesian networks
- Markov models
- constraint satisfaction
- machine learning
- reinforcement learning
- neural networks
- natural language processing
Students learn about the theory behind graph search algorithms, categorization, optimization, reinforcement learning, and other artificial intelligence and machine learning topics through hands-on projects that they incorporate into their own Python applications.
By the end of the course, you will have gained familiarity with machine learning libraries as well as knowledge of artificial intelligence principles, allowing you to create your own intelligent systems.