This project was completed as a part of the Honors portion of the Advanced Learning Algorithms Course on Coursera.
Credit to DeepLearning.AI, Stanford, and the Coursera platform for providing the course materials and guidance.
This laboratory session delves into the examination and enhancement of machine learning models. Through this exploration, various techniques to evaluate the model's performance and optimize it will be uncovered. One crucial aspect is understanding the process of splitting the dataset, which offers valuable insights into the model's behavior in a real-world production environment. Additionally, this procedure provides valuable guidance on how to fine-tune and improve the model effectively. By the end of this report, a comprehensive understanding of evaluating and optimizing machine learning models will be achieved, enabling better-informed decisions in future model development endeavors.
