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Course Description

This course is designed for students who have completed AI 1 and are familiar with Python programming language. The course expands on the topics covered in AI 1 such as Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA), and explores their applications in different scenarios. For instance, the course covers how to handle missing data and how to apply PCA and LDA to datasets beyond just face images. Additionally, the course introduces students to the Tensorflow library and delves into the concept of Time Series and Sequences, which are important for training AI models.

The course also provides ample detail on Natural Language Processing (NLP) so that students can comprehend how chatbots operate. To achieve this, the course uses various libraries such as sklearn, pandas, numpy, and matplotlib, and incorporates pre-built models within the sklearn library. The course material also focuses on teaching the practical application of these concepts in modern technologies.

By the end of the course, students should have a solid grasp of the material covered, including advanced topics such as Time Series, Sequences, and NLP. The students should be able to apply the concepts learned in real-world scenarios, and have hands-on experience working with libraries such as sklearn and Tensorflow. Overall, this course aims to provide students with a comprehensive understanding of the most recent AI technologies, preparing them for a career in the field.