Data Explorer: Machine Learning Basics

This course provides a comprehensive introduction to data analysis and basic machine learning concepts. It starts with the fundamentals of data representation and manipulation using Python and Colab notebooks. Students learn to load, process, and visualize various types of data. The course then progresses to exploring relationships between variables, introducing linear and polynomial regression models. Students learn how to evaluate model quality using metrics like mean-squared error and R-squared values. The curriculum also covers important statistical concepts such as data distributions, means, and standard deviations, as well as the impact of noise on data analysis. In the final module, students are introduced to classification algorithms, including linear regression for classification, support vector machines, and logistic regression. Throughout the course, emphasis is placed on practical application, with students working on hands-on projects and exercises to reinforce their learning.