Towards the end of my second semester of sophomore year, I began taking a Mathematics for Machine Learning course on Coursera, which covered the basics of linear algebra, multivariate calculus, and principal component analysis. Although the classification algorithms I am using are implemented from calling functions already defined in imported libraries, this course helped me gain a deeper understanding of the mathematical processes, such as matrix transformations, that occur behind the scenes of those functions.

In the summer before my junior year, I took another course called Machine Learning A-Z on Udemy to learn about the use cases and implementations of many machine learning algorithms, from as simple as linear regression to as complex as convolutional neural networks. This course taught me necessary data preprocessing skills, model evaluation techniques, and most importantly, how to implement the classification and clustering algorithms I plan to use in my project (which are Random Forest, Logistic Regression, and K-Nearest Neighbors for classification, along with K-means clustering and Hierarchical clustering).

Read more about what I learned in my coursework here.