Things on this page are fragmentary and immature notes/thoughts of the author. Please read with your own judgement!
Platforms
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
- 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
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Lecture 16 | Machine Learning (Stanford) | Reinforcement Learning
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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
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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
-
https://github.com/academic/awesome-datascience
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Essential Cheat Sheets for Machine Learning and Deep Learning Engineers
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https://rushter.com/dsreader/