A portfolio of Jupyter Notebooks demonstrating various machine learning concepts and models learned and developed during my graduate machine learning course and independently post-grad. Generally, a bottom-up modeling approach with Numpy is used to develop both a conceptual grasp of the mathematical foundation of ML and of the model architecture. The use of higher-level libraries (e.g. scikit-learn, Keras) with optimized implementations of algorithms are generally used during model evaluation and comparison.