This repo includes machine learning and data mining models I implemented in Python and R.

Most of the examples are for the two machine learning courses CS5800, CN229, and two data mining courses CS6930 and GR4058 that I took.

Models implemented and demonstrated in this repo includes:

1. Maximum Likelihood Estimator
2. Naive Bayes (Maximum A Posterior)
3. K Nearest Neighbors
4. Logistic Regression
5. Linear Regression (L1, L2)
6. Decision Tree
7. Random Forest
8. Clustering: k-means, DBSCAN
9. Support Vector Machine (svm)
10. Perceptron

Models that are mainly used for big data mining includes:

1. Apriori algorithm
2. FP-tree
3. Dynamic Time Warping (DTW)
4. Autoregression

• Fordham [CS5800] Machine Learning(https://storm.cis.fordham.edu/~leeds/cisc5800/)

• Fordham CS6930

• Stanford CN229: Machine Learning, Youtube

• Stanford Statistical Learning

• Stanford CS246: Mining Massive Dataset, Youtube

• CMU 15781 - Tom Mitchell Machine Learning

• CMU 10701 Introduction to Machine Learning

• CMU CS-589 Topics in Machine Learning Theory

• Columbia COMS4771 Machine Learning

• Columbia W4995 Applied Machine Learning

• NYU/Bloomberg Machine Learning, Video

• Google ML Crash Course

• Andrew Ng Machine Learning Yearning