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2020 Intro to DS Module 0 - Learning Objectives.pdf
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2020 Intro to DS Module 1 - Becoming a Data Scientist.pdf
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2020 Intro to DS Module 10 - Empirical Risk Minimization Primer.pdf
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2020 Intro to DS Module 11 - Logistic Regression.pdf
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2020 Intro to DS Module 12 - Support Vector Machines.pdf
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2020 Intro to DS Module 13 - ModelSelection.pdf
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2020 Intro to DS Module 14 - Solution Engineering.pdf
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2020 Intro to DS Module 15 - Evaluation.pdf
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2020 Intro to DS Module 16 - Decision Science.pdf
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2020 Intro to DS Module 17 - Labeling and Sampling.pdf
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2020 Intro to DS Module 18 - Feature Engineering.pdf
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2020 Intro to DS Module 19 - FeatureSelection.pdf
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2020 Intro to DS Module 2 - Data Mining Overview.pdf
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2020 Intro to DS Module 20 - Regularization.pdf
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2020 Intro to DS Module 21 - Text Processing.pdf
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2020 Intro to DS Module 22 - Naive Bayes.pdf
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2020 Intro to DS Module 23 - Spam Detection.pdf
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2020 Intro to DS Module 24 - Ensembles Stacking.pdf
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2020 Intro to DS Module 25 - Ensembles Random Forest.pdf
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2020 Intro to DS Module 26 - Ensembles Gradient Boosting.pdf
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2020 Intro to DS Module 27 - kNN.pdf
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2020 Intro to DS Module 28 - Approximate NN.pdf
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2020 Intro to DS Module 29 - Clustering.pdf
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2020 Intro to DS Module 3 - Understanding Data.pdf
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2020 Intro to DS Module 4 - Sampling and Distributional Biases.pdf
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2020 Intro to DS Module 5 - Getting Started EDA.pdf
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2020 Intro to DS Module 6 - Bivariate Structure.pdf
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2020 Intro to DS Module 7 - Multivariate Structure.pdf
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2020 Intro to DS Module 8 - Supervised Learning Primer.pdf
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2020 Intro to DS Module 9 - Decision Trees.pdf
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