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/-73.99253082,40.75297546,13,0,0/550x200?access_token=pk.eyJ1IjoiZ2FyeXNndWlkZSIsImEiOiJjamtobmRiYmgwb3gwM3F1a2o2Mmd0YWw3In0.DinA62CR3-OKtf3axa5O7Q)
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| DETAILS |
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This 20-hour Machine Learning with Python course covers all the basic machine learning methods & Python modules (especially Scikit-Learn) for implementing them. The five sessions cover: simple & multiple Linear regressions; classification methods including logistic regression, discriminant analysis & naive bayes, support vector machines (SVMs) & tree based methods; cross-validation & feature selection; regularization; principal component analysis (PCA) & clustering algorithms. After successfully completing of this course, you will be able to explain the principles of machine learning algorithms & implement these methods to analyze complex datasets & make predictions in Python.
Instructors
Reece Heineke is Director of Big Data at a fintech startup. His wanderlust has led him near & far, with a stop in the UK where he completed a PhD in Astrophysics at the University of Cambridge before embarking on a career in quantitative finance in Chicago, London & now New York City. He is excited to join the Academy's team to be able to teach once again. In addition to data & computer science, Reece is passionate about snowboarding, long distance running & bread baking.
Gordon has a B.A in Pure Mathematics & a M.A. in Applied Mathematics from CUNY Queens College. He worked for two startups, but most of his experience is in academia-the latest being as an Adjunct Mathematics Lecturer. He is currently a Data Analyst at New Classrooms where the goal is to insight into how to improve Personalized Learning. Gordon is equally comfortable with both the Python & R Data Science toolboxes.
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