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Studying statistics is a core a part of your journey towards turning into an information scientist, knowledge analyst, and even an AI engineer. The vast majority of the machine studying fashions utilized in trendy know-how are statistical fashions. So, having a robust understanding of statistics will make it simpler so that you can be taught and construct superior AI applied sciences.
On this weblog, we are going to 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 Knowledge Scientists
Repository: gedeck/practical-statistics-for-data-scientists
This repository gives sensible examples and code snippets from the e book “Practical Statistics for Data Scientists” that cowl important statistical methods and ideas. It’s a nice place to begin for knowledge scientists who wish to apply statistical strategies in real-world situations.
The e book’s code repository comprises correct R and Python code examples. In case you are used to the Jupyter Pocket book type of coding, it additionally supplies 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 supplies an interactive, hands-on introduction to Bayesian strategies utilizing Python. The content material is introduced as Jupyter notebooks utilizing nbviewer, making it simple to observe concept and Python code about Bayesian fashions and probabilistic programming.
The interactive e book consists of an introduction to Bayesian strategies, getting began with Python’s PyMC library, Markov Chain Monte Carlo, the legislation of huge 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 consists of complete documentation and examples for performing numerous statistical exams, linear fashions, time sequence evaluation, and extra. We will use these examples from the documentation to learn 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/likelihood
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 superb useful resource for these focused on 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 set of recipes for fixing widespread statistical issues, serving as a useful reference for locating fast options and examples for numerous statistical duties. It supplies concise steerage for likelihood and statistics, together with ideas akin to steady distribution, likelihood concept, random variables, expectation, variance, and inequalities. You may both use the make command to entry the cookbook regionally or obtain the PDF file. The repository additionally consists of LaTeX information for the varied statistical ideas.
6. Seeing Idea
Repository: seeingtheory/Seeing-Idea
Seeing Idea is a visible introduction to likelihood and statistics. This repository consists of interactive visualizations and explanations that make complicated statistical ideas extra accessible and simpler to know, particularly for visible learners.
It’s a extremely interactive e book for rookies and covers numerous matters akin to primary likelihood, compound likelihood, likelihood 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 protecting normal statistics, mathematical programming, and scientific computing utilizing Python. It’s a useful useful resource for anybody trying to strengthen their statistical and mathematical programming expertise.
It consists of 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 consists of code examples and Jupyter notebooks from the e book “Python for Probability, Statistics, and Machine Learning” that cowl a variety of matters, from primary likelihood and statistics to superior machine studying methods.
Throughout the “chapters” folder, there are three subfolders containing Jupyter notebooks on statistics, likelihood, and machine studying. Every pocket book consists of 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 useful reference for college kids and professionals.
It’s a well-liked cheatsheet that covers matters on conditional likelihood, 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 enable you be taught the fundamentals of algebra, calculus, statistics, likelihood, vectors, and matrices via Python Jupyter Notebooks.
Remaining Ideas
Studying assets shared on GitHub are created by consultants and the open-source group, aiming to share their information to pave a better path for rookies within the fields of information science and statistics. You’ll be taught statistics by studying concept, fixing code examples, understanding mathematical ideas, constructing initiatives, performing numerous analyses, and exploring well-liked statistical instruments. All of those are coated within the GitHub repository talked about above. These assets are free, and anybody can contribute to enhance them. So, continue to learn and preserve constructing wonderful issues.
Abid Ali Awan (@1abidaliawan) is an authorized knowledge scientist skilled who loves constructing machine studying fashions. At the moment, 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 kids combating psychological sickness.
