kahartman2
Data scientist passionate about solving problems with a robust analytical approach.
New York, NY
Pinned Repositories
311_Project
applying-gradient-descent-data-science-intro-000
applying-gradient-descent-lab-data-science-intro-000
applying-nearest-neighbors-data-science-intro-000
calculating-distance-data-science-intro-000
calculating-distance-lab-data-science-intro-000
coffee_classification
county_level_drug_impact
nyc_eats_dash
nyc_ems_calls
kahartman2's Repositories
kahartman2/county_level_drug_impact
kahartman2/nyc_ems_calls
kahartman2/COVID-19
Novel Coronavirus (COVID-19) Cases, provided by JHU CSSE
kahartman2/dsc-0-05-14-grouping-data-lab-nyc-career-ds-102218
kahartman2/dsc-1-05-05-selecting-data-nyc-career-ds-102218
kahartman2/dsc-1-05-06-selecting-data-lab-nyc-career-ds-102218
kahartman2/dsc-1-05-07-filtering-and-ordering-nyc-career-ds-102218
kahartman2/dsc-1-05-08-filtering-and-ordering-lab-nyc-career-ds-102218
kahartman2/dsc-1-05-09-introduction-to-table-relationships-nyc-career-ds-102218
kahartman2/dsc-1-05-10-join-statements-nyc-career-ds-102218
kahartman2/dsc-1-05-12-one-to-many-and-many-to-many-joins-nyc-career-ds-102218
kahartman2/dsc-1-08-14-combinations-lab-nyc-career-ds-102218
kahartman2/dsc-2-13-15-linalg-regression-lab-nyc-career-ds-102218
kahartman2/dsc-2-14-15-gradient-to-cost-function-nyc-career-ds-102218
kahartman2/dsc-2-14-16-applying-gradient-descent-lab-nyc-career-ds-102218
kahartman2/dsc-2-19-08-poisson-distribution-lab-nyc-career-ds-102218
kahartman2/dsc-2-21-03-conditional-prob-nyc-career-ds-102218
kahartman2/dsc-2-21-10-monty-hall-lab-nyc-career-ds-102218
kahartman2/dsc-3-26-08-sarima-models-lab-nyc-career-ds-102218
kahartman2/dsc-3-30-07-logistic-regression-model-comparisons-nyc-career-ds-102218
kahartman2/dsc-3-31-08-decision-tree-pruning-hyperparameter-optimization-lab-nyc-career-ds-102218
kahartman2/dsc-3-31-08-decision-tree-pruning-hyperparameter-optimization-nyc-career-ds-102218
kahartman2/dsc-3-31-10-regression-cart-trees-lab-nyc-career-ds-102218
kahartman2/dsc-3-32-03-random-forests-nyc-career-ds-102218
kahartman2/dsc-3-32-04-tree-ensembles-and-random-forests-lab-nyc-career-ds-102218
kahartman2/dsc-3-32-07-gradient-boosting-and-weak-learners-nyc-career-ds-102218
kahartman2/dsc-3-32-09-gradient-boosting-lab-nyc-career-ds-102218
kahartman2/dsc-3-33-02-introduction-to-support-vector-machines-nyc-career-ds-102218
kahartman2/dsc-3-33-05-the-kernel-trick-nyc-career-ds-102218
kahartman2/sagemaker-run-notebook
Tools to run Jupyter notebooks as jobs in Amazon SageMaker - ad hoc, on a schedule, or in response to events