I Introduction

1 Welcome

II Background

2 How To Talk About Data in Machine Learning

3 Algorithms Learn a Mapping From Input to Output

4 Parametric and Nonparametric Machine Learning Algorithms

5 Supervised, Unsupervised and Semi-Supervised Learning

6 The Bias-Variance Trade-O

7 Overtting and Undertting

III Linear Algorithms

8 Crash-Course in Spreadsheet Math

9 Gradient Descent For Machine Learning

10 Linear Regression

11 Simple Linear Regression Tutorial

12 Linear Regression Tutorial Using Gradient Descent

13 Logistic Regression

14 Logistic Regression Tutorial

15 Linear Discriminant Analysis

16 Linear Discriminant Analysis Tutorial

IV Nonlinear Algorithms

17 Classication and Regression Trees

18 Classication and Regression Trees Tutorial

19 Naive Bayes

20 Naive Bayes Tutorial

21 Gaussian Naive Bayes Tutorial

22 K-Nearest Neighbors

23 K-Nearest Neighbors Tutorial

24 Learning Vector Quantization

25 Learning Vector Quantization Tutorial

26 Support Vector Machines

27 Support Vector Machine Tutorial

V Ensemble Algorithms

28 Bagging and Random Forest

29 Bagged Decision Trees Tutorial

30 Boosting and AdaBoost

31 AdaBoost Tutorial

VI Conclusions

32 How Far You Have Come

33 Getting More Help