- http://d2l.ai/
- https://montreal.ai/ai4all.pdf?fbclid=IwAR0lcuy46muQ0-SmwgfP34WBRoSP8os2qi4eeR8kex_68Nh1aTJndfpIUgk
- Datasets & Models -- https://paperswithcode.com/methods
- Q & As -- https://github.com/christianversloot/machine-learning-articles/blob/main/differences-between-autoregressive-autoencoding-and-sequence-to-sequence-models-in-machine-learning.md
Quick overview and calculator:
Really good high-level with examples:
Overview and Quick analysis:
- https://www.scribbr.com/category/statistics/
- https://mgimond.github.io/Stats-in-R/CI.html
- https://www.statsmodels.org/devel/examples/notebooks/generated/regression_diagnostics.html
- http://web.vu.lt/mif/a.buteikis/wp-content/uploads/PE_Book/3-4-UnivarMLE.html
Fair & Unfair coin:
Expected payout and gambling problems:
- https://www.gamblingsites.com/blog/14-gambling-probability-examples-14437/
- https://people.math.umass.edu/~lr7q/ps_files/teaching/math456/Week5.pdf
Best Book (If you got time):
All distributions:
- http://www.math.wm.edu/~leemis/chart/UDR/UDR.html
- https://medium.com/@srowen/common-probability-distributions-347e6b945ce4
Nice visual ML and in depth :
Casual Inference:
- https://causalinference.gitlab.io/
- https://www.linkedin.com/posts/leihuaye_experimentation-causalinference-datascience-activity-6852331407507824640-zsPW
Pre-reqs:
Challenges in ML modeling:
- https://github.com/zhiqiangzhongddu/Data-Science-Interview-Questions-and-Answers-General-
(linear models, trees, neural networks and others) \ - https://github.com/alexeygrigorev/data-science-interviews/blob/master/theory.md
(SQL, Python, coding) \ - https://github.com/alexeygrigorev/data-science-interviews/blob/master/technical.md
- http://www.silota.com/docs/recipes/sql-histogram-summary-frequency-distribution.html
- https://www.stratascratch.com/blog/500-sql-interview-questions-from-real-companies/
- https://www.interviewquery.com/courses/data-science-course/lessons/sql/1f1c4c1b-43a1-4ac3-8c33-583713b15eb6
(R - understand concepts)
https://daviddalpiaz.github.io/appliedstats/applied_statistics.pdf
https://www.crumplab.com/statistics/ -- Awesome book
Bayesian modeling complete guide
https://github.com/fonnesbeck/Bayes_Computing_Course
PGM/Bayesian Nets
https://www.cs.ubc.ca/~murphyk/Bayes/bnintro.html
Multilevel modeling using Bayesian methods
https://nbviewer.ipython.org/github/fonnesbeck/multilevel_modeling/blob/master/multilevel_modeling.ipynb
A/B testing
- https://www.udacity.com/course/ab-testing--ud257
- https://github.com/thomastt2020/Udacity-A-B-Test/blob/master/Analyze_ab_test_results_notebook.ipynb
- https://hbr.org/2020/03/productive-innovation#avoid-the-pitfalls-of-a-b-testing
MLOps
- https://huyenchip.com/mlops/
- https://chicagodatascience.github.io/MLOps/logistics/
- https://madewithml.com/
- https://www.deeplearning.ai/program/machine-learning-engineering-for-production-mlops/
- Notes -- https://github.com/kennethleungty/MLOps-Specialization-Notes
Deep Generative Models
- https://deepgenerativemodels.github.io/notes/index.html
- https://lilianweng.github.io/lil-log/2018/08/12/from-autoencoder-to-beta-vae.html
- VAEs awesome - https://jxmo.io/posts/variational-autoencoders
Markov Chains:
- https://www.stat.berkeley.edu/~aldous/150/takis_exercises.pdf \
- https://vknight.org/OR_Methods/Markov_Chains/Markov_Chains_Exercise_Sheet-Solutions.pdf \
- http://web.math.ku.dk/~susanne/kursusstokproc/ProblemsMarkovChains.pdf *No answers
Additional part:
INTERESTING PROBLEMS:
https://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.674.1678&rep=rep1&type=pdf