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Mart van de Ven edited this page Feb 11, 2014
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- Lesson 01 : Unix and Python Foundations
- Lesson 02 : Linear Algebra/Matrix Algebra Review
- Lesson 03 : Python Libraries
- Lesson 04 : Intro to database structures and MySQL
- Lesson 14 : Data Exploration and Experimental Design
- Lesson 05 : Linear Regressions
- Lesson 06 : Polynomial and Logistic Regressions
- Lesson 07 : KNN Classification
- Lesson 08 : Probability, Naive Bayes, AUC
- Lesson 09 : Decision Trees, Confusion Matrices, Precision/Recall
- Lesson 10 : Support Vector Machines
- Lesson 11 : K-Means Clustering
- Lesson 12 : (Kernel) Principal Component Analysis and Singular Value Decomposition
- Lesson 13 : Ensemble Methods, Adaboost, and Random Forests
- Lesson 15 : Time Series Analytics and Prediction
- Lesson 16 : Distributed Systems, MapReduce and Hadoop
- TBC
- Regressions: Prediction Baseball Salaries
- Classification: Predicting Bad Used Car Purchases
- Brief and Grading Details
- Proposals
- Data Exploration and Abstracts
- Reading Journals and Writing Journal Reviews
- Metrics for Regressions
- Next Steps
- Recommended Reading