Recommendations
Every now and then, people ask me about programming/statistics/machine learning recommendations in general. This page is a compilation of things I usually recommend in such situations.
See also this link.
Programming:
R
python
c++
Machine Learning/Statistics
Bayesian stuff
Miscellaneous Machine Learning
- NN from scratch
- Probabilistic Graphical Models notes
- GLM notes
- Variable Selection by Andrew Parnell
- Feature Engineering
- Chris Albon’s notes
- Visualisation references
- MCMC examples
- ML by Larry Wasserman
- Fast AI
- tidymodels
- MLSS 2019 London - Full Videos
- Isak’s summary - MLSS 2019
- Variational Inference
- Mathematical Tours
Probability and Statistics
Coursera courses
Books
Book recommendations listed here.