As a practicing data scientist and machine learning educator, I’m always taking courses. Suppose you want to go directly to applications, learn the most current software and get a job. In that case, I recommend my IBM Data Science Professional Certificate or IBM Machine Learning Professional Certificate.

 But sometimes, you want to get deeper into the mathematics, prove every detail, and develop the algorithms from scratch. If that’s the case, check out Kilian Weinberger’s free course, it’s comprehensive and he’s funny as hell.

Kilian Weinberger: image source

Kilian Weinberger is an Associate Professor in the Department of Computer Science at Cornell University. He received his Ph.D. from the University of Pennsylvania in Machine Learning. The youtube link is here, and the notes are listed here :

Course Notes

1. Machine Learning Setup
2. k-Nearest Neighbors / Curse of Dimensionality
3. Perceptron
4. Estimating Probabilities from data
5. Bayes Classifier and Naive Bayes
6. Logistic Regression / Maximum Likelihood Estimation / Maximum a Posteriori
7. Gradient Descent
8. Linear Regression
9. Support Vector Machine
10. Empirical Risk Minimization
11. Model Selection
12. Bias-Variance Tradeoff
13. Kernels
14. Kernels continued
15. Gaussian Processes
16. k-Dimensional Trees
17. Decision Trees
18. Bagging
19. Boosting
20. Neural Networks
21. Deep Learning / Stochastic Gradient Descent

I really liked his work on Bagging and Boosting!