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.
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Portfolio of Jupyter Notebooks demonstrating various ML models/concepts learned and developed during my graduate machine learning course and independently post-grad. Generally, a bottom-up modeling approach with Numpy is used to showcase grasp of mathematical foundation. Higher-level libraries (scikit-learn) used for optimized algo implementations.
alecpippas/Machine-Learning-Portfolio
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Portfolio of Jupyter Notebooks demonstrating various ML models/concepts learned and developed during my graduate machine learning course and independently post-grad. Generally, a bottom-up modeling approach with Numpy is used to showcase grasp of mathematical foundation. Higher-level libraries (scikit-learn) used for optimized algo implementations.
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