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14# Ensembles: Stacking, Super Learner, Subsemble
25- Overview
36- What is Ensemble Learning?
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15# Introduction to Data Science, Machine Learning & Predictive Analytics
26- Overview
37- What is Data Science?
48- Data Science Tasks
5- - Problem Formulation
9+ - Problem Formulation (motivation)
610 - Collect & Process Data
711 - Machine Learning
812 - Insights & Action
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120124- Training Data:
121125- Features:
122126- Models:
127+ - Model Validation:
128+ - Overfitting: ?
123129- Supervised Learning:
124130- Unsupervised Learning:
125131
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166172
167173![ DS Google Trends] ( images/ml_workflow.png )
168174
175+ Data scientist built a black-box model. Need a cluster to productionize model. Network pipeline bringing data in (IT and Hardware involved)
176+
177+ TO DO: Add a slide about how this fits into the IT pipeline and business.
178+
169179
170180## References
171181[ 1] [ https://www.reddit.com/r/MachineLearning/comments/2fxi6v/ama_michael_i_jordan/ckelmtt ] ( https://www.reddit.com/r/MachineLearning/comments/2fxi6v/ama_michael_i_jordan/ckelmtt )
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14# Topics in Model Selection
25- Overview
6+ - Performance Metrics
7+ - Supervised
8+ - Regression
9+ - Binomial Classification
10+ - Multinomial Classification
11+ - Ranking
12+ - Unsupervised
313- Train & Test
14+ TO DO: Code example
415- Cross-validation
5- - Grid Search
6- - Performance Metrics
16+ TO DO: Code example
17+ - Hyperparameter Search
18+ TO DO: Code example
19+ - Grid (cartesian)
20+ - Random
21+ - etc
22+ - Model interpretability
23+ TO DO: Code example
724
825
926## Overview
1027
28+ ## Performance Metrics
29+
1130## Train & Test
1231
1332## Cross-validation
1433
15- ## Grid Search
16-
17- ## Performance Metrics
34+ ## Hyperparameter Search
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