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Learn to distinguish supervised, unsupervised, and deep learning, evaluate algorithm performance, choose algorithms for investment problems, and apply the CFA Ethical Decision-Making Framework to machine learning dilemmas.
Learn statistical measures, dispersion, distributions, sampling and hypothesis testing on investment data, and build visualizations in Python to present a clear data story.
Learn the statistics behind machine learning for investing: probability, linear and logistic regression, gradient descent, and time-series models, with accompanying Python and R code explained.
Learn to tell data stories, build Python visualizations, apply probability and time-series models to investing, and explain machine learning applications and their ethical dilemmas.
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