Machine Learning Library are framework that allows users to easily understand and implement models of machines by making the procedures involved in acquiring data easy, using models for training users, providing predictions, and refining the results expected in the future. Machine learning is closely related to computational statistics, which also focuses on prediction-making through the use of computers. It has strong ties to mathematical optimization, which delivers methods, theory and application domains to the field. Machine learning can also be unsupervised and be used to learn and establish baseline behavioral profiles for various entities and then used to find meaningful anomalies. It combines models and algorithms; therefore, simplifying the way the users learn about the machines. The system also offers an added advantage for developers who want to debug. It analyzes data and mines data by using simple and effective tools that can be used by the user easily. The software is easily accessible and can be reused in various contexts. It offers several powerful features all with an aim of simplifying how users understand models of machines. It is capable of identifying categories to which objects belong to. Through this property, users can apply it in recognition of images and detection of spam. The framework also can predict attributes associated with an object and group similar objects into groups automatically. Users can use this software to reduce the quantity of random variables to be considered. By using the selection of models feature, users can validate, select, and compare models and parameters.