https://github.com/valatwork/statistics
Putting together study material from various sources, from linear algebra to machine learning
https://github.com/valatwork/statistics
calculus learning linear-algebra machine-learning probability python pytorch statistics tensorflow
Last synced: about 2 months ago
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Putting together study material from various sources, from linear algebra to machine learning
- Host: GitHub
- URL: https://github.com/valatwork/statistics
- Owner: valatwork
- License: other
- Created: 2025-04-04T13:29:47.000Z (about 1 year ago)
- Default Branch: main
- Last Pushed: 2025-05-09T19:40:24.000Z (about 1 year ago)
- Last Synced: 2025-08-09T02:29:51.662Z (11 months ago)
- Topics: calculus, learning, linear-algebra, machine-learning, probability, python, pytorch, statistics, tensorflow
- Language: Jupyter Notebook
- Homepage:
- Size: 54.9 MB
- Stars: 1
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE.md
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README
# Statistics, Probability and Machine Learning
# Table of contents
## I - Optional Study Resources
[I.I - Linear Algebra](https://github.com/valatwork/linear-algebra)
[I.II - Calculus](https://github.com/valatwork/calculus)
## 1 - Introduction
[1.0 Terminology and basic concepts](/project/001-intro.md)
## 2 - Descriptive Statistics
### 2.1 - Measures of Central Tendency
[2.1.1 - Mean](/project/002-mean.ipynb)
[2.1.2 - Median](/project/003-median.ipynb)
[2.1.3 - Midrange](/project/004-midrange.ipynb)
[2.1.4 - Mode](/project/005-mode.ipynb)
### 2.2 - Measures of Shape
[2.2.1 - Skewness](/project/006-skewness.ipynb)
[2.2.2 - Kurtosis](/project/007-kurtosis.ipynb)
### 2.3 - Measures of Dispersion
[2.3.1 - Measures of Dispersion - Intro](/project/008-measures-of-dispersion.md)
[2.3.2 - Sample vs Population](/project/009-sample-vs-population.md)
[2.3.3 - Range](/project/010-range.ipynb)
[2.3.4 - Variance](/project/011-variance.ipynb)
[2.3.5 - Standard Deviation](/project/012-standard-deviation.ipynb)
[2.3.6 - Scaling and shifting](/project/013-scaling-and-shifting.md)
## 3 - Probability
[3.1 - Introduction to Probability](/project/014-probability-intro.md)
[3.2 - Set Operations](/project/015-set-operations.ipynb)
[3.3 - (Some) Rules of Probability](/project/016-probability-rules.md)
[3.4 - Combinatorics](/project/017-combinatorics.ipynb)
[3.5 - Probability Exercises](/project/018-probability-exercises.ipynb)
## 4 - Probability Distributions
[4.1 - Introduction to Probability Distributions](/project/019-distributions-intro.md)
[4.2 - Covariance](/project/020-covariance.ipynb)
[4.3 - Correlation](/project/021-correlation-intro.ipynb)
[4.4 - Probability Mass Function and Probability Density Function](/project/022-pmf-and-pdf.ipynb)
[4.5 - Expected Value](/project/023-expected-value.ipynb)
[4.6 - Marginal Probability](/project/024-marginal-probability.md)
[4.7 - Uniform Distribution](/project/025-uniform-distribution.ipynb)
[4.8 - Normal Distribution](/project/026-normal-distribution.ipynb)
[4.9 - Central Limit Theorem](/project/027-central-limit-theorem.ipynb)
[4.10 - Exponential and Laplace Distributions](/project/028-exponential-and-laplace.ipynb)
[4.11 - Bernoulli, Binomial, Multinomial Distributions](/project/029-bernoulli-binomial-multinomial.ipynb)
[4.12 - Poisson Distribution](/project/030-poisson-distribution.ipynb)
## 5 - Inferential Statistics
### 5.1 Introduction
[5.1.1 - Entropy](/project/031-entropy.ipynb)
[5.1.2 - Statistics and Machine Learning](/project/032-statistics-and-ML.md)
### 5.2 Hypothesis Testing
[5.2.1 - Hypothesis Testing](/project/033-hypothesis-testing.md)
[5.2.2 - Z-Score](/project/034-z-scores.ipynb)
[5.2.3 - P-Value](/project/035-p-value.ipynb)
[5.2.4 - Single Sample t-tests](/project/036-single-sample-t-test.ipynb)
[5.2.5 - Independent t-tests](/project/037-independent-t-tests.ipynb)
[5.2.6 - Paired t-tests](/project/038-paired-t-tests.ipynb)
[5.2.7 - Confidence Intervals](/project/039-confidence-intervals.ipynb)
[5.2.8 - ANOVA](/project/040-anova.ipynb)
[5.2.9 - Correlation (expanded): Pearson's Correlation](/project/041-correlation-2-pearson.ipynb)
## 6 - Machine Learning
### 6.1 - Supervised Learning
#### 6.1.1 - Regression
[6.1.1.1 - Independent vs Dependent variables](/project/042-independent-vs-dependent-variables.ipynb)
[6.1.1.2 - Linear Regression](/project/043-linear-regression.ipynb)
[6.1.1.3 - Ordinary Least Squares (OLS)](/project/044-ordinary-least-squares.ipynb)
[6.1.1.4 - Multiple Linear Regression](/project/045-multiple-linear-regression.ipynb)
[6.1.1.5 - Cost Function, Gradient Descent, Residuals](/project/046-cost-functions-gradient-descent-residuals.ipynb)
[6.1.1.6 - Polynomial Regression](/project/047-polynomial-regression.ipynb)
#### 6.1.2 - Classification
[6.1.2.1 - Regularization, Feature Scaling, Cross Validation](/project/048-regularization-feature-scaling-cross-validation.md)
[6.1.2.2 - Ridge Regression, Lasso Regression, Elastic Net](/project/049-ridge-lasso-elastic-net.ipynb)
[6.1.2.3 - Feature Engineering](/project/050-feature-engineering.ipynb)
[6.1.2.4 - Cross Validation and Grid Search](/project/051-cross-validation-grid-search.ipynb)
[6.1.2.5 - Logistic Regression](/project/052-logistic-regression.ipynb)
[6.1.2.6 - k-Nearest Neighbors (kNN)](/project/053-k-nearest-neighbors.ipynb)
[6.1.2.7 - Support Vector Machines (SVM) and Support Vector Regression (SVR)](/project/054-support-vector-machines.ipynb)
[6.1.2.8 - Decision Trees](/project/055-decision-trees.ipynb)
[6.1.2.9 - Random Forests](/project/056-ensemble-learning-random-forests.ipynb)
[6.1.2.10 - Boosting Methods](/project/057-boosting-methods.ipynb)
[6.1.2.11 - Naive Bayes and Natural Language Processing](/project/058-naive-bayes-and-nlp.ipynb)
### 6.2 - Dimensionality Reduction
[6.2.1 - Dimensionality Reduction](/project/059-dimensionality-reduction.ipynb)
[6.2.2 - Principal Component Analysis (PCA)](/project/060-principal-component-analysis.ipynb)
### 6.3 - Unsupervised Learning
[6.3.1 - k-Means Clustering](/project/061-k-means-clustering.ipynb)
[6.3.2 - Gaussian Mixtures](/project/062-gaussian-mixtures.ipynb)
[6.3.3 - Hierarchical Clustering](/project/063-hierarchical-clustering.ipynb)
[6.3.4 - DBSCAN](/project/064-dbscan.ipynb)
## Appendices
[Appendix A - Sources](project/999A-sources.md)
[Appendix X - Python Reference](project/999B-python-reference.md)
[Appendix Y - Subset Selection Theory](project/999Y-subset-selection-theory.ipynb)
[Appendix Z - Variable Types Examples](project/999Z-variable-types.md)