{"id":27162921,"url":"https://github.com/valatwork/statistics","last_synced_at":"2026-05-06T06:31:37.974Z","repository":{"id":286625903,"uuid":"960462603","full_name":"valatwork/statistics","owner":"valatwork","description":"Putting together study material from various sources, from linear algebra to machine learning","archived":false,"fork":false,"pushed_at":"2025-05-09T19:40:24.000Z","size":57550,"stargazers_count":1,"open_issues_count":0,"forks_count":0,"subscribers_count":1,"default_branch":"main","last_synced_at":"2025-08-09T02:29:51.662Z","etag":null,"topics":["calculus","learning","linear-algebra","machine-learning","probability","python","pytorch","statistics","tensorflow"],"latest_commit_sha":null,"homepage":"","language":"Jupyter Notebook","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"other","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/valatwork.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":"LICENSE.md","code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null,"roadmap":null,"authors":null,"dei":null,"publiccode":null,"codemeta":null}},"created_at":"2025-04-04T13:29:47.000Z","updated_at":"2025-05-09T19:40:27.000Z","dependencies_parsed_at":"2025-04-07T15:32:27.249Z","dependency_job_id":null,"html_url":"https://github.com/valatwork/statistics","commit_stats":null,"previous_names":["valatwork/statistics"],"tags_count":0,"template":false,"template_full_name":null,"purl":"pkg:github/valatwork/statistics","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/valatwork%2Fstatistics","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/valatwork%2Fstatistics/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/valatwork%2Fstatistics/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/valatwork%2Fstatistics/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/valatwork","download_url":"https://codeload.github.com/valatwork/statistics/tar.gz/refs/heads/main","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/valatwork%2Fstatistics/sbom","scorecard":null,"host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":275185158,"owners_count":25419916,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2022-07-04T15:15:14.044Z","status":"online","status_checked_at":"2025-09-14T02:00:10.474Z","response_time":75,"last_error":null,"robots_txt_status":"success","robots_txt_updated_at":"2025-07-24T06:49:26.215Z","robots_txt_url":"https://github.com/robots.txt","online":true,"can_crawl_api":true,"host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":["calculus","learning","linear-algebra","machine-learning","probability","python","pytorch","statistics","tensorflow"],"created_at":"2025-04-09T01:34:26.719Z","updated_at":"2026-05-06T06:31:37.926Z","avatar_url":"https://github.com/valatwork.png","language":"Jupyter Notebook","funding_links":[],"categories":[],"sub_categories":[],"readme":"# Statistics, Probability and Machine Learning\n\n# Table of contents\n\n## I - Optional Study Resources\n\n[I.I - Linear Algebra](https://github.com/valatwork/linear-algebra)\n\n[I.II - Calculus](https://github.com/valatwork/calculus)\n\n\n## 1 - Introduction\n[1.0 Terminology and basic concepts](/project/001-intro.md)\n\n## 2 - Descriptive Statistics\n### 2.1 -  Measures of Central Tendency\n[2.1.1 - Mean](/project/002-mean.ipynb)\n\n[2.1.2 - Median](/project/003-median.ipynb)\n\n[2.1.3 - Midrange](/project/004-midrange.ipynb)\n\n[2.1.4 - Mode](/project/005-mode.ipynb)\n\n### 2.2 - Measures of Shape\n[2.2.1 - Skewness](/project/006-skewness.ipynb)\n\n[2.2.2 - Kurtosis](/project/007-kurtosis.ipynb)\n\n### 2.3 - Measures of Dispersion\n[2.3.1 - Measures of Dispersion - Intro](/project/008-measures-of-dispersion.md)\n\n[2.3.2 - Sample vs Population](/project/009-sample-vs-population.md)\n\n[2.3.3 - Range](/project/010-range.ipynb)\n\n[2.3.4 - Variance](/project/011-variance.ipynb)\n\n[2.3.5 - Standard Deviation](/project/012-standard-deviation.ipynb)\n\n[2.3.6 - Scaling and shifting](/project/013-scaling-and-shifting.md)\n\n## 3 - Probability\n[3.1 - Introduction to Probability](/project/014-probability-intro.md)\n\n[3.2 - Set Operations](/project/015-set-operations.ipynb)\n\n[3.3 - (Some) Rules of Probability](/project/016-probability-rules.md)\n\n[3.4 - Combinatorics](/project/017-combinatorics.ipynb)\n\n[3.5 - Probability Exercises](/project/018-probability-exercises.ipynb)\n\n## 4 - Probability Distributions\n\n[4.1 - Introduction to Probability Distributions](/project/019-distributions-intro.md)\n\n[4.2 - Covariance](/project/020-covariance.ipynb)\n\n[4.3 - Correlation](/project/021-correlation-intro.ipynb)\n\n[4.4 - Probability Mass Function and Probability Density Function](/project/022-pmf-and-pdf.ipynb)\n\n[4.5 - Expected Value](/project/023-expected-value.ipynb)\n\n[4.6 - Marginal Probability](/project/024-marginal-probability.md)\n\n[4.7 - Uniform Distribution](/project/025-uniform-distribution.ipynb)\n\n[4.8 - Normal Distribution](/project/026-normal-distribution.ipynb)\n\n[4.9 - Central Limit Theorem](/project/027-central-limit-theorem.ipynb)\n\n[4.10 - Exponential and Laplace Distributions](/project/028-exponential-and-laplace.ipynb)\n\n[4.11 - Bernoulli, Binomial, Multinomial Distributions](/project/029-bernoulli-binomial-multinomial.ipynb)\n\n[4.12 - Poisson Distribution](/project/030-poisson-distribution.ipynb)\n\n## 5 - Inferential Statistics\n\n### 5.1 Introduction\n\n[5.1.1 - Entropy](/project/031-entropy.ipynb)\n\n[5.1.2 - Statistics and Machine Learning](/project/032-statistics-and-ML.md)\n\n### 5.2 Hypothesis Testing\n\n[5.2.1 - Hypothesis Testing](/project/033-hypothesis-testing.md)\n\n[5.2.2 - Z-Score](/project/034-z-scores.ipynb)\n\n[5.2.3 - P-Value](/project/035-p-value.ipynb)\n\n[5.2.4 - Single Sample t-tests](/project/036-single-sample-t-test.ipynb)\n\n[5.2.5 - Independent t-tests](/project/037-independent-t-tests.ipynb)\n\n[5.2.6 - Paired t-tests](/project/038-paired-t-tests.ipynb)\n\n[5.2.7 - Confidence Intervals](/project/039-confidence-intervals.ipynb)\n\n[5.2.8 - ANOVA](/project/040-anova.ipynb)\n\n[5.2.9 - Correlation (expanded): Pearson's Correlation](/project/041-correlation-2-pearson.ipynb)\n\n## 6 - Machine Learning\n\n### 6.1  - Supervised Learning\n\n#### 6.1.1 - Regression\n\n[6.1.1.1 - Independent vs Dependent variables](/project/042-independent-vs-dependent-variables.ipynb)\n\n[6.1.1.2 - Linear Regression](/project/043-linear-regression.ipynb)\n\n[6.1.1.3 - Ordinary Least Squares (OLS)](/project/044-ordinary-least-squares.ipynb)\n\n[6.1.1.4 - Multiple Linear Regression](/project/045-multiple-linear-regression.ipynb)\n\n[6.1.1.5 - Cost Function, Gradient Descent, Residuals](/project/046-cost-functions-gradient-descent-residuals.ipynb)\n\n[6.1.1.6 - Polynomial Regression](/project/047-polynomial-regression.ipynb)\n\n#### 6.1.2 - Classification\n\n[6.1.2.1 - Regularization, Feature Scaling, Cross Validation](/project/048-regularization-feature-scaling-cross-validation.md)\n\n[6.1.2.2 - Ridge Regression, Lasso Regression, Elastic Net](/project/049-ridge-lasso-elastic-net.ipynb)\n\n[6.1.2.3 - Feature Engineering](/project/050-feature-engineering.ipynb)\n\n[6.1.2.4 - Cross Validation and Grid Search](/project/051-cross-validation-grid-search.ipynb)\n\n[6.1.2.5 - Logistic Regression](/project/052-logistic-regression.ipynb)\n\n[6.1.2.6 - k-Nearest Neighbors (kNN)](/project/053-k-nearest-neighbors.ipynb)\n\n[6.1.2.7 - Support Vector Machines (SVM) and Support Vector Regression (SVR)](/project/054-support-vector-machines.ipynb)\n\n[6.1.2.8 - Decision Trees](/project/055-decision-trees.ipynb)\n\n[6.1.2.9 - Random Forests](/project/056-ensemble-learning-random-forests.ipynb)\n\n[6.1.2.10 - Boosting Methods](/project/057-boosting-methods.ipynb)\n\n[6.1.2.11 - Naive Bayes and Natural Language Processing](/project/058-naive-bayes-and-nlp.ipynb)\n\n### 6.2 - Dimensionality Reduction\n\n[6.2.1 - Dimensionality Reduction](/project/059-dimensionality-reduction.ipynb)\n\n[6.2.2 - Principal Component Analysis (PCA)](/project/060-principal-component-analysis.ipynb)\n\n### 6.3 - Unsupervised Learning\n\n[6.3.1 - k-Means Clustering](/project/061-k-means-clustering.ipynb)\n\n[6.3.2 - Gaussian Mixtures](/project/062-gaussian-mixtures.ipynb)\n\n[6.3.3 - Hierarchical Clustering](/project/063-hierarchical-clustering.ipynb)\n\n[6.3.4 - DBSCAN](/project/064-dbscan.ipynb)\n\n\n## Appendices\n\n[Appendix A - Sources](project/999A-sources.md)\n\n[Appendix X - Python Reference](project/999B-python-reference.md)\n\n[Appendix Y - Subset Selection Theory](project/999Y-subset-selection-theory.ipynb)\n\n[Appendix Z - Variable Types Examples](project/999Z-variable-types.md)","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fvalatwork%2Fstatistics","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fvalatwork%2Fstatistics","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fvalatwork%2Fstatistics/lists"}