Foundational machine learning techniques—from data preprocessing to supervised and unsupervised algorithms.
This work was completed as part of the Bertelsmann Tech Scholarship program on Udacity, focused on developing practical machine learning and AI skills.
CharityML is a fictitious charity organization that aims to provide financial support to individuals interested in learning machine learning. After sending nearly 32,000 letters, the organization observed that most donations came from individuals earning more than $50,000 annually.
Goal: Identify the most effective machine learning model to maximize donation yield.
Deep learning models are increasingly used in real-world applications, including image recognition systems in mobile and web applications. These models are trained on large datasets to accurately identify and classify visual patterns.
Goal: Train a deep learning model to classify different species of flowers.
This project uses demographic data to explore patterns within a population and identify distinct customer groups. By applying clustering techniques, it becomes possible to better understand customer behavior and distribution across segments.
Goal: Analyze demographic features, group individuals into clusters, and identify how customers are distributed across these segments.
Completion certificate from the Bertelsmann Tech Scholarship program on Udacity.
