Alexander
Levin-Koopman
As a lecturer in Applied Data Science at the University of Michigan School of Information, I'm passionate about solving problems through insights extracted from complex data sets. With a Master's degree in Applied Data Science (4.0 GPA) from the University of Michigan and a Bachelor's degree in Mathematics from the University of Colorado at Boulder, I've developed a strong foundation in statistical analysis, machine learning, and data visualization.
My academic background has equipped me with a unique ability to approach problems from multiple angles, think creatively, and communicate complex ideas effectively. While my experience is rooted in academia, I'm eager to apply my skills in a real-world setting and drive business outcomes through data-driven decision making.
I thrive on solving intricate problems and uncovering hidden patterns in data. My goal is to leverage my expertise to inform strategic decisions, optimize processes, and create value for organizations. If you're looking for a driven and analytical problem-solver who is passionate about data science, let's connect!
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ML-From-Scratch
Project Description: This is a collection of notebooks designed to provide a deep understanding of how ML Models work. These implementations are meant for educational purposes and should not be used in production. They are created in base python with only the use of the builtin random and typing modules. Any use of external libraries are only for demonstration and comparison purposes.
Unsupervised Learning
K-means clustering is used to find clusters or segmented groups of data points from unlabeled data. It does this by first initializing cluster seeds or centroids. We then iterate through the points and find their closest centroid and assign them to it. Then we take the mean of the points assigned to each centroid and update the location of that centroid to the mean. This process is repeated until a convergence condition is met. Convergence here is defined as the the total squared distance moved by each centroid being less than a pre-defined threshold.