Applied Machine Learning: Introduction
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YouTubeStanford Graduate School of Business
Applied Machine Learning: Introduction
Professor Jann Spiess presents an introduction to applied machine learning.
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How I taught myself ML without a CS degree. Here’s the complete self-taught curriculum I used to become a Data Scientist / Applied Scientist / ML Scientist (save this for interview prep): Machine Learning: “Introduction to Statistical Learning” has all the fundamentals you need for ML interviews. - Linear regression, classification, decision trees - Ensemble methods (bagging, boosting, random forest) - Regularization, loss functions, bias-variance tradeoff - Cross-validation, gradient descent -
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