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Studying statistics is a core a part of your journey towards changing into a knowledge scientist, knowledge analyst, and even an AI engineer. The vast majority of the machine studying fashions utilized in fashionable know-how are statistical fashions. So, having a powerful understanding of statistics will make it simpler so that you can study and construct superior AI applied sciences.
On this weblog, we’ll discover 10 GitHub repositories that can assist you grasp statistics. These repositories embody code examples, books, Python libraries, guides, documentations, and visible studying supplies.
1. Sensible Statistics for Information Scientists
Repository: gedeck/practical-statistics-for-data-scientists
This repository provides sensible examples and code snippets from the guide “Sensible Statistics for Information Scientists” that cowl important statistical strategies and ideas. It’s a nice place to begin for knowledge scientists who wish to apply statistical strategies in real-world situations.
The guide’s code repository comprises correct R and Python code examples. If you’re used to the Jupyter Pocket book model of coding, it additionally offers related examples in a Jupyter Pocket book for Python and R.
2. Probabilistic Programming and Bayesian Strategies for Hackers
Repository: CamDavidsonPilon/Probabilistic-Programming-and-Bayesian-Strategies-for-Hackers
This repository offers an interactive, hands-on introduction to Bayesian strategies utilizing Python. The content material is offered as Jupyter notebooks utilizing nbviewer, making it straightforward to comply with principle and Python code about Bayesian fashions and probabilistic programming.
The interactive guide consists of an introduction to Bayesian strategies, getting began with Python’s PyMC library, Markov Chain Monte Carlo, the regulation of enormous numbers, loss capabilities, and extra.
3. Statsmodels: Statistical Modeling and Econometrics in Python
Repository: statsmodels/statsmodels
Statsmodels is a robust library for statistical modeling and econometrics in Python. This repository contains complete documentation and examples for performing varied statistical checks, linear fashions, time sequence evaluation, and extra. We are able to use these examples from the documentation to discover ways to carry out all types of statistical evaluation, together with time sequence evaluation, survival evaluation, multivariate evaluation, linear regression, and extra.
4. TensorFlow Likelihood
Repository: tensorflow/chance
TensorFlow Likelihood is a library for probabilistic reasoning and statistical evaluation in TensorFlow. It extends TensorFlow core library with instruments for constructing and coaching probabilistic fashions, making it a wonderful useful resource for these considering combining deep studying with statistical modeling.
The documentation comprises examples of linear combined results fashions, hierarchical linear fashions, probabilistic principal parts evaluation, bayesian neural networks, and extra.
5. The Likelihood and Statistics Cookbook
Repository: mavam/stat-cookbook
This repository is a group of recipes for fixing widespread statistical issues, serving as a useful reference for locating fast options and examples for varied statistical duties. It offers concise steerage for chance and statistics, together with ideas corresponding to steady distribution, chance principle, random variables, expectation, variance, and inequalities. You possibly can both use the make command to entry the cookbook domestically or obtain the PDF file. The repository additionally contains LaTeX information for the assorted statistical ideas.
6. Seeing Idea
Repository: seeingtheory/Seeing-Idea
Seeing Idea is a visible introduction to chance and statistics. This repository contains interactive visualizations and explanations that make complicated statistical ideas extra accessible and simpler to grasp, particularly for visible learners.
It’s a extremely interactive guide for newcomers and covers varied subjects corresponding to fundamental chance, compound chance, chance distributions, frequentist inference, bayesian inference, and regression evaluation.
7. Stats Maths with Python
Repository: tirthajyoti/Stats-Maths-with-Python
This repository comprises scripts and Jupyter notebooks masking basic statistics, mathematical programming, and scientific computing utilizing Python. It’s a helpful useful resource for anybody seeking to strengthen their statistical and mathematical programming expertise.
It contains the examples on bayes rule, brownian movement, speculation testing, linear regression, and extra.
8. Python for Likelihood, Statistics, and Machine Studying
Repository: unpingco/Python-for-Likelihood-Statistics-and-Machine-Studying
This repository contains code examples and Jupyter notebooks from the guide “Python for Likelihood, Statistics, and Machine Studying” that cowl a variety of subjects, from fundamental chance and statistics to superior machine studying strategies.
Throughout the “chapters” folder, there are three subfolders containing Jupyter notebooks on statistics, chance, and machine studying. Every pocket book contains code, output, and an outline explaining the methodology, code, and outcomes.
9. Likelihood and Statistics VIP Cheatsheets
Repository: shervinea/stanford-cme-106-probability-and-statistics
This repository comprises VIP cheatsheets for Stanford’s Likelihood and Statistics for Engineers course. The cheatsheets present concise summaries of key ideas and formulation, making them a helpful reference for college students and professionals.
It’s a fashionable cheatsheet that covers subjects on conditional chance, random variables, parameter estimation, speculation testing, and extra.
10. Fundamental Arithmetic for Machine Studying
Repository: hrnbot/Fundamental-Arithmetic-for-Machine-Studying
Understanding the mathematical foundations is essential for mastering machine studying and statistics. This repository goals to demystify arithmetic and allow you to study the fundamentals of algebra, calculus, statistics, chance, vectors, and matrices by Python Jupyter Notebooks.
Last Ideas
Studying sources shared on GitHub are created by consultants and the open-source group, aiming to share their data to pave a better path for newcomers within the fields of information science and statistics. You’ll study statistics by studying principle, fixing code examples, understanding mathematical ideas, constructing tasks, performing varied analyses, and exploring fashionable statistical instruments. All of those are lined within the GitHub repository talked about above. These sources are free, and anybody can contribute to enhance them. So, continue learning and hold constructing wonderful issues.
Abid Ali Awan (@1abidaliawan) is an authorized knowledge scientist skilled who loves constructing machine studying fashions. At present, he’s specializing in content material creation and writing technical blogs on machine studying and knowledge science applied sciences. Abid holds a Grasp’s diploma in know-how administration and a bachelor’s diploma in telecommunication engineering. His imaginative and prescient is to construct an AI product utilizing a graph neural community for college students fighting psychological sickness.
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