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Machine Learning and AI Foundations: Classification Modeling

Aug 20, 2018 • Keith McCormick

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About this course

Classification methods are among the most important in modern data science. Learn classification strategies and algorithms for machining learning and AI.



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Classification problems in machine learning

35s
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What you should know

1m 39s
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Defining terms

3m 3s
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The importance of binary classification

1m 47s
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Binary vs. multinomial

3m 41s
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So-called “black box” techniques

3m 2s
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One task, many algorithms

2m 31s
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Statistics vs. machine learning

2m 37s
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Model assessment vs. business evaluation

1m 29s
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Training and test partitions

3m 4s
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Lift Charts

1m 50s
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Gains tables

2m 7s
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Confusion matrix

3m 19s
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Overview

2m 10s
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Discriminant with three categories

5m 44s
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Discriminant with two categories

5m 2s
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Stepwise discriminant

1m 3s
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Logistic regression

10m 54s
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Stepwise logistic regression

1m 3s
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Decision Trees

4m 46s
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KNN

3m 58s
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Linear SVM

8m 2s
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Neural nets

7m 57s
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Bayesian networks

7m 54s
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Heterogenous ensembles

3m 22s
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Bagging and random forest

3m 26s
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Boosting and XGBoost

1m 57s
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Imbalanced target categories

3m 7s
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Interactions

4m 22s
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Missing data

3m
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Bias-variance trade-off and overfitting

5m 54s
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Data reduction

6m 54s
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AutoML

2m 58s
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Next steps

1m 9s