Machine Learning for High Schoolers

Nowadays, machine learning and AI seem to be buzzwords that are often thrown around. Conceptually, it's straightforward - you give a computer a bunch of data to "learn" from, which provides it with the capability to make predictions on data it has not seen before. However, the inner mechanics of this calculus-based, meaning only very advanced undergraduates or college students are able to comprehend it. My goal in this project was to fix that issue, by providing everyone with free resources providing both the intuition and mathematical background behind machine learning concepts, all the way up to basic neural networks. This was done in a manner so that it would be understandable by anyone with the most basic knowledge and skills in mathematics.

After creating a set of five educational articles covering the topic, I also added four labs that implemented the concepts covered in the article it was linked in. These labs included solutions hidden in the form of dropdowns, as well as boilerplate code to start off. Only the basic libraries were imported - namely numpy and pandas - as the goal of these labs is to learn the algorithms behind the broad concepts. After this, I conducted a study to determine the efficacy of these articles, from which both qualitative and quantitative data was collected, which resulted in a positive outcome both qualitatively and quantitatively. Finally, I wrote a paper summarizing my work as well as the study and its conclusions.