The domain of machine learning and deep learning is constantly evolving, and in the recent past, privacy concerns have gained significant attention. This project dives into differential privacy and its integration into machine learning, with the primary focus being the Private Aggregation of Teacher Ensembles (PATE) framework. The privacy guarantees that an algorithm can provide are measured by the Differential Privacy framework. The machine learning algorithms that can be used to train models on private data can be designed by utilizing the differential privacy framework. PATE, at the heart of this project, is a very powerful tool in machine learning that integrates the preservation of privacy. The project explores the implementation and the theory behind the implementation of PATE for training models on sensitive data while guarding individual privacy. The project explores the core principles of differential privacy, the aggregation of teacher models, and the training of a student model for the rigorous preservation of privacy. The construction and training of the machine learning model aims to protect privacy while balancing accuracy and utility, the two fundamental aspects of any model. The results derived from the project aim to provide insights into the domain of privacy-preserving machine learning.