Machine Learning Resources
Things on this page are fragmentary and immature notes/thoughts of the author. Please read with your own judgement!
https://azure.microsoft.com/en-us/services/virtual-machines/data-science-virtual-machines/
Google TPU
Amazon
Misc¶
https://github.com/bulutyazilim/awesome-datascience
Data Mining/Machine Learning¶
Stanford Machine Learning
Machine Learning Video Library
Learning from Data
Big Data Clustering, Anil Jain
Big Data Clustering, Anil Jain
The Elements of Statistical Learning
StatNotes
UCLA
Statistical Computing
Elementary Statistical Concept
RDataMining
Lecture 5 | Machine Learning (Stanford) | Naive Bayesian Classification with an Example of Spam Email Prediction
Lecture 6 | Machine Learning (Stanford) | Naive Baysian, Neuron Network, SVM
Lecture 7 | Machine Learning (Stanford) | SVM, optimal margin classifier, primary/dual optimization, KKT, Kenel
Lecture 8 | Machine Learning (Stanford) | SVM, Kenel
Lecture 9 | Machine Learning (Stanford) | Learning Theories
Lecture 10 | Machine Learning (Stanford) | Learning Theories, Variable Selection
Lecture 11 | Machine Learning (Stanford) | Tips for Machine Learning
Lecture 12 | Machine Learning (Stanford) | Unsupervised Learning
Lecture 13 | Machine Learning (Stanford) | EM algorithm
Lecture 14 | Machine Learning (Stanford) | Factor Analysis, PCA
Lecture 15 | Machine Learning (Stanford) | PCA, ICA
Lecture 16 | Machine Learning (Stanford) | Reinforcement Learning
Lecture 19 | Machine Learning (Stanford) | Advice for Machine Learning
Decision Tree (1)
Decision Tree (2)
Ensemble/Aggregation (1) Basics
Ensemble/Aggregation (2) Bagging
Ensemble/Aggregation (3) Gradient Boosting
Ensemble/Aggregation (1)
Random Forests Theory and Applications for Variable Selection - Video 1 of 5
Random Forests Theory and Applications for Variable Selection - Video 2 of 5
References¶