Applied Machine Learning is the practical implementation of machine learning techniques to solve real-world problems. It involves the development of algorithms and models that can learn from data and make predictions or decisions based on that learning. Applied machine learning can be used in various domains such as healthcare, finance, e-commerce, and others to make predictions, optimize processes, and automate decision making. Federated learning is a machine learning approach that enables multiple devices or edge nodes to collaborate on a machine learning model without requiring the data to be centralized. In federated learning, the model is trained on data that is distributed across multiple devices or edge nodes, with each device contributing to the model’s development without sharing its data. This approach addresses privacy concerns and reduces data transfer requirements while still allowing for the development of accurate and efficient machine learning models. Applied machine learning and federated learning are two critical components of the rapidly evolving field of machine learning, enabling practical and efficient solutions to a wide range of problems in various domains.