|Project Name||Stars||Downloads||Repos Using This||Packages Using This||Most Recent Commit||Total Releases||Latest Release||Open Issues||License||Language|
|Probability||3,917||126||213||5 days ago||43||June 07, 2022||634||apache-2.0||Jupyter Notebook|
|Probabilistic reasoning and statistical analysis in TensorFlow|
|Math Php||2,222||36||22||18 days ago||132||April 10, 2022||51||mit||PHP|
|Powerful modern math library for PHP: Features descriptive statistics and regressions; Continuous and discrete probability distributions; Linear algebra with matrices and vectors, Numerical analysis; special mathematical functions; Algebra|
|Stat Cookbook||2,102||4 months ago||2||other||TeX|
|:orange_book: The probability and statistics cookbook|
|Www.mlcompendium.com||1,991||12 days ago|
|The Machine Learning & Deep Learning Compendium was a list of references in my private & single document, which I curated in order to expand my knowledge, it is now an open knowledge-sharing project compiled using Gitbook.|
|Ml Foundations||1,705||7 months ago||mit||Jupyter Notebook|
|Machine Learning Foundations: Linear Algebra, Calculus, Statistics & Computer Science|
|Data Science Roadmap||1,462||12 days ago||1||mit|
|Data Science Roadmap from A to Z|
|Hackermath||1,339||6 years ago||5||mit||Jupyter Notebook|
|Introduction to Statistics and Basics of Mathematics for Data Science - The Hacker's Way|
|Seeing Theory||1,043||4 years ago||22||apache-2.0||HTML|
|A visual introduction to probability and statistics.|
|Distributions.jl||992||766||9 days ago||January 02, 2021||361||other||Julia|
|A Julia package for probability distributions and associated functions.|
|Kotlin Statistics||825||4||3||a year ago||7||December 27, 2018||15||apache-2.0||Kotlin|
|Idiomatic statistical operators for Kotlin|
Second edition of Springer text Python for Probability, Statistics, and Machine Learning
This book, fully updated for Python version 3.6+, covers the key ideas that link probability, statistics, and machine learning illustrated using Python modules in these areas. All the figures and numerical results are reproducible using the Python codes provided. The author develops key intuitions in machine learning by working meaningful examples using multiple analytical methods and Python codes, thereby connecting theoretical concepts to concrete implementations. Detailed proofs for certain important results are also provided. Modern Python modules like Pandas, Sympy, Scikit-learn, Tensorflow, and Keras are applied to simulate and visualize important machine learning concepts like the bias/variance trade-off, cross-validation, and regularization. Many abstract mathematical ideas, such as convergence in probability theory, are developed and illustrated with numerical examples.
This updated edition now includes the Fisher Exact Test and the Mann-Whitney-Wilcoxon Test. A new section on survival analysis has been included as well as substantial development of Generalized Linear Models. The new deep learning section for image processing includes an in-depth discussion of gradient descent methods that underpin all deep learning algorithms. As with the prior edition, there are new and updated Programming Tips that the illustrate effective Python modules and methods for scientific programming and machine learning. There are 445 run-able code blocks with corresponding outputs that have been tested for accuracy. Over 158 graphical visualizations (almost all generated using Python) illustrate the concepts that are developed both in code and in mathematics. We also discuss and use key Python modules such as Numpy, Scikit-learn, Sympy, Scipy, Lifelines, CvxPy, Theano, Matplotlib, Pandas, Tensorflow, Statsmodels, and Keras.
This book is suitable for anyone with an undergraduate-level exposure to probability, statistics, or machine learning and with rudimentary knowledge of Python programming.
If you are using
conda, you can get started by cloning this
repository and using the
environment.yaml file as in the
conda env create -n pyPSML -f environment.yaml
and then activate the environment using the following,
conda activate pyPSML
Then, you can run
jupyter notebook and navigate the Jupyter
notebooks for the individual chapters. All of the notebooks are fully
functional in this so-created environment. Note that there are
embedded figures in the Jupyter notebooks that are meant to validate
the outputs of the Matplotlib codes therein.
If you are using docker, there is a
Dockerfile included. After cloning
this repository, you can build the image with the following,
docker build -t pypsml2e .
and then run it locally using,
docker container run -it -p 8888:8888 pypsml2e
Then, navigate to the output URL and you can explore the Jupyter notebooks for each chapter. Alternately, if you don't want to build your own image, you can do
docker run -p 8888:8888 unpingco/pypsml2e
to get the docker image from https://hub.docker.com/r/unpingco/pypsml2e. Note that this may not be as up-to-date as building it yourself from this repository, but should still work fine.
Your comments (including errata) are welcome in the Issues link above.
Good luck! I hope you find these materials helpful.