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Course Description

This course is designed to introduce students to the world of Artificial Intelligence and its various applications. The course assumes that students have completed Python Level 3 and have a strong foundation in Python programming. The course starts by introducing the application of AI through the use of naive functions which are functions written in the programming language's in-built libraries and not third-party libraries. This approach helps the student to focus on how the code is applied rather than contending with advanced math concepts, assuming they are comfortable with reading and writing Python code. However, any mathematical concepts used in the class are explained in the lesson.

The course then moves on to the application of computer vision using OpenCV. Other libraries such as sk-learn, numpy, and matplotlib are also introduced in the course, although no prior knowledge of these libraries is required. The course covers some advanced techniques such as Principal Component Analysis (PCA), which is used in dimensionality reduction and face reconstruction, and is contrasted with Linear Discriminant Analysis (LDA). The primary focus of the course is on how these techniques are applied in computer vision using images as the primary data.

By the end of the course, students are expected to have a good understanding of how the learned concepts apply to modern technologies. The course provides a solid foundation for students to explore the various areas of Artificial Intelligence and its applications. Students will have the necessary skills and knowledge to apply AI concepts to real-world problems and create their own AI-powered applications. Overall, this course is an excellent choice for anyone looking to dive into the world of Artificial Intelligence and explore its various applications.