Day 1: Introduction to Machine Learning
Day 2: ML Modelling
Day 3: Data Modelling
Day 4: Regression : Simple, Multiple and Polynomial
Day 5: Types of regression: Ridge, Lasso & Elastic Net
Day 6: Logistic Regression
Day 7: Decision Tree
Day 8: Random Forest & Ensemble Learning
Day 9: KNN & Naive Bayes
Day10: SVM
Day11: Unsupervised Learning: Kmeans, Kmedoid, Agglomerative, divisive, DBSCAN
Day12: Association: Apriori, Elcat, + Dimension Reduction
Day13: FP growth + Time series analysis
Day14: NLP
Day15:

http://www.tramy.us/numpybook.pdf

http://docs.scipy.org/doc/