MachineLearningDemos: Some demos for maching learning.

• Classifying with k-Nearest Neighbors
• Splitting datasets one feature at a time: decision trees
• Classifying with probability theory: naive Bayes
• Logisit regression
• Support vecor machines
• Improving classification with AdaBoost meta-algorithm

• Predicting numeric valuses: regression
• Tree-based regression

• Grouping unlabeled items using k-means clustering
• Association analysis with the Apriori algorithm
• Efficiently finding frequent itemsets with FP-growth

• Using pincipal component analysis to simplify data
• Simplifying data with singular value decomposition
• Big data and MapReduce