Unlock Learning Potential with Expert Online Tutors Anytime, Anywhere!……………………

The session involved a tutor and student discussing the final module of an Artificial Intelligence course titled "Model Deployment." The student expressed a need to understand the practical application and coding associated with deploying AI models, and they planned to connect later in the week to work on the associated projects after the student reviewed some introductory material.

The session focused on relational database concepts, specifically one-to-one, one-to-many, and many-to-many relationships between entities. The Student practiced identifying these relationships using real-world examples and learned about cardinality as a tool for determining relationship types. As homework, the Student was assigned to create one example for each relationship type (one-to-one, one-to-many, and many-to-many).

The session introduced the fundamental concepts of Relational Database Management Systems (RDBMS), specifically focusing on the types of relationships between tables: one-to-one, one-to-many, and many-to-many. The student practiced identifying and understanding these relationships through examples and learned about the importance of atomicity in database design, with a plan to cover normalization and Power BI modeling in subsequent sessions.

The Student revised data analysis techniques using Pandas, focusing on data cleaning and preparation. They worked through a pre-existing notebook on Uber data, converting data types, handling missing values, and extracting date components. The next session will cover Matplotlib and Seaborn for data visualization.

The Student and Tutor reviewed cross-validation techniques, including different types like k-fold and stratified cross-validation. They also discussed methods for handling imbalanced datasets such as over-sampling and under-sampling. The Tutor assigned further review of sampling techniques and model tuning in preparation for the upcoming quiz.

The session involved guided practice on Pandas, focusing on merging, concatenating, and data exploration techniques. The student worked through a worksheet in Google Colab, loading datasets and performing various data analysis tasks. The student was assigned to review the material covered in the session before the next class.