Verzeo

During my internship at Verzeo, I collaborated with four talented team members on an innovative project to develop a machine learning model capable of detecting a person's age based on their facial features. Leveraging the power of convolutional neural networks (CNNs), we meticulously designed and implemented an architecture that could accurately analyze and interpret facial data. Together, we selected appropriate layers, tuned hyperparameters, and ensured the model's robustness to various facial expressions and angles.

Our journey began with an extensive phase of data collection and preprocessing. We sourced diverse datasets to ensure the model's accuracy across different demographics and lighting conditions. Preprocessing involved standardizing the images, detecting and aligning faces, and augmenting the data to simulate real-world variability. By incorporating techniques like rotation, scaling, and flipping, we significantly enhanced the model's generalization capabilities, making it more resilient to unseen data.

Upon achieving promising results, we presented our findings and the model's performance to the project team and stakeholders. The presentations were detailed, highlighting the model's accuracy, precision, and potential applications. By demonstrating the practical implications and benefits of the model, we effectively communicated its value to the team. This experience not only honed my technical skills but also enhanced my ability to work collaboratively and convey complex information clearly and persuasively.