With the abundance of data at our fingertips, data-driven solutions are being created to solve our modern problems. For the past couple of years, the Covid-19 pandemic has been a major global issue, and as a result, machine learning methods have been used in response. For this post, I will summarize the 2020 article Applications and challenges of AI-based algorithms in the COVID-19 pandemic by Danai Khemasuwan and Henri G Colt. In this literature review, the researchers explored the strengths and limitations of how machine learning algorithms are being used to respond to the Covid-19 pandemic. This is relevant not only in regard to Covid-19 but also in other instances where algorithms and public health intersect.

Over the course of the quarter, I alongside my group Kyle Fang, Adhvaith Vijay, and Britney Zhao created a webapp for users who are learning about dog breeds. In this reflection, the first four sections were written as a group, and I wrote the last two sections individually.

In this post, I’ll explain how to create a fake news classifier using TensorFlow. We will be creating three models to predict whether news articles contain fake news, and then evaluate them.

In this post I will be guiding you through the process of creating a simple spectral clustering algorithm. Spectral clustering allows us to find similarities in data points. In this simple tutorial, we will understand how to partition data into their natural clusters.

In this post I will be demonstrating the functions I wrote to query climate data from the NOAA and create interactive visualizations.

In this post I will be giving a tutorial on how to use Python to make an interesting scatterplot from the Palmer Penguins dataset. This dataset contains information about 344 penguins and we will create a scatterplot that compares the flipper length and body mass of the penguins based on their species.