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https://github.com/dn070017/data-science-resources
Resources and tutorial for data science
https://github.com/dn070017/data-science-resources
data-science deep-learning machine-learning statistics
Last synced: 29 days ago
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Resources and tutorial for data science
- Host: GitHub
- URL: https://github.com/dn070017/data-science-resources
- Owner: dn070017
- Created: 2019-06-07T08:29:40.000Z (over 5 years ago)
- Default Branch: master
- Last Pushed: 2020-12-16T21:36:00.000Z (about 4 years ago)
- Last Synced: 2024-11-14T04:28:34.282Z (3 months ago)
- Topics: data-science, deep-learning, machine-learning, statistics
- Homepage: https://hackmd.io/@Xg9_wrttQju8FXRCNT-Baw
- Size: 5.86 KB
- Stars: 1
- Watchers: 0
- Forks: 3
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
Awesome Lists containing this project
README
# Data Science Resources
## Introduction
This repository contains some learning notes, materials and resources for the mathematical concept and theory of data science. You are welcome to contribute to uncover missing fields or make recommendation to this repository. Please contact me if the content posted here is incorrect or the link has expired. I would also like to give credit to all the authors who dedicated to preparing these articles and tutorials. Hope these resources can provide useful references for data scientists.
## Table of Content
* [Linear Algebra](#linear-algebra)
* [Metrics](#metrics)
* [Data Normalization](#data-normalization)
* [Feature Engineering](#feature-engineering)
* [Dimension Reduction](#dimension-reduction)
* [Regression Model](#regression-model)
* [Clustering](#clustering)
* [Bayesian Inference](#bayesian-inference)
* [Frequent Pattern Mining](#frequent-pattern-mining)
* [Interpretable Machine Learning](#interpretable-machine-learning)
* [Online Courses](#online-courses)
## Linear Algebra
* [Basics (HackMD)](https://hackmd.io/@Xg9_wrttQju8FXRCNT-Baw/SyVE8OeWr)
* [LU Decomposition (HackMD)](https://hackmd.io/@Xg9_wrttQju8FXRCNT-Baw/ry4r-9eZB)
* [LU Decomposition (Wikipedia)](https://en.wikipedia.org/wiki/LU_decomposition)
* [QR Decomposition (HackMD)](https://hackmd.io/@Xg9_wrttQju8FXRCNT-Baw/ry4r-9eZB)
* [QR Decomposition (Wikipedia)](https://en.wikipedia.org/wiki/QR_decomposition)
* [Eigendecomposition (HackMD)](https://hackmd.io/@Xg9_wrttQju8FXRCNT-Baw/HJxf_9_bH)
* [Eigendecomposition (MIT 18.065)](https://www.youtube.com/watch?v=k095NdrHxY4)
* [Singular Value Decomposition (HackMD)](https://hackmd.io/@Xg9_wrttQju8FXRCNT-Baw/HJxf_9_bH)
* [Singluar Value Decomposition (Stanford CS341)](https://www.youtube.com/watch?v=P5mlg91as1c)
* [Singular Value Decomposition (MIT 18.065)](https://www.youtube.com/watch?v=rYz83XPxiZo)
* [Singular Value Decomposition (Jonathan Hui)](https://medium.com/@jonathan_hui/machine-learning-singular-value-decomposition-svd-principal-component-analysis-pca-1d45e885e491)
[Back to Top](#introduction)
## Metrics
* [Cross Entropy (Naoki Shibuya)](https://towardsdatascience.com/demystifying-cross-entropy-e80e3ad54a8)
* [Distances and Similarity (HackMD)](https://hackmd.io/@Xg9_wrttQju8FXRCNT-Baw/BkYRDtwR4)
* [Entropy (Naoki Shibuya)](https://towardsdatascience.com/demystifying-entropy-f2c3221e2550)
* [Kullback-Leibler Divergence (Naoki Shibuya)](https://towardsdatascience.com/demystifying-kl-divergence-7ebe4317ee68)
* [Precision and Recall (Wikipedia)](https://en.wikipedia.org/wiki/Precision_and_recall#Definition_(classification_context))
* [Precision and Recall (Georgios Drakos)](https://towardsdatascience.com/how-to-select-the-right-evaluation-metric-for-machine-learning-models-part-3-classification-3eac420ec991)
* [Harmonic Mean (Wikipedia)](https://en.wikipedia.org/wiki/Harmonic_mean)
* [Correlation Coefficients (HackMD)](https://hackmd.io/@Xg9_wrttQju8FXRCNT-Baw/HyHDRsUmB)
* [Silhouette Index (Wikipedia)](https://en.wikipedia.org/wiki/Silhouette_(clustering))
* [Dunn Index (Wikipedia)](https://en.wikipedia.org/wiki/Dunn_index)
* [Clustering Validation Metrics (Alboukadel Kassambara)](https://www.datanovia.com/en/lessons/cluster-validation-statistics-must-know-methods/)
[Back to Top](#introduction)
## Data Normalization
* [General Introduction (Sebastian Raschka)](http://sebastianraschka.com/Articles/2014_about_feature_scaling.html)
[Back to Top](#introduction)
## Feature Engineering
* [Mean Encoding (Miguel José Monteiro)](https://towardsdatascience.com/why-you-should-try-mean-encoding-17057262cd0)
* [Mean Encoding (Sangarshanan)](https://medium.com/datadriveninvestor/improve-your-classification-models-using-mean-target-encoding-a3d573df31e8)
[Back to Top](#introduction)
## Parameter Optimization
* [Bayesian Hyperparameter Optimization (by Will Koehrsen)](https://towardsdatascience.com/a-conceptual-explanation-of-bayesian-model-based-hyperparameter-optimization-for-machine-learning-b8172278050f)
[Back to Top](#introduction)
## Regression Model
* [Ridge, Lasso and Elastic Net (Gurkamal Deol)](https://hackernoon.com/an-introduction-to-ridge-lasso-and-elastic-net-regression-cca60b4b934f)
* [Stability Selection (Shiqiong Huang and Micol Marchetti-Bowick)](https://www.stat.cmu.edu/~ryantibs/journalclub/stability.pdf)
[Back to Top](#introduction)
## Clustering
* [Affinity Propagation (Ritchie Vink)](https://www.ritchievink.com/blog/2018/05/18/algorithm-breakdown-affinity-propagation/)
* [DBSCAN (Evan Lutins)](https://medium.com/@elutins/dbscan-what-is-it-when-to-use-it-how-to-use-it-8bd506293818)
* [HDBSCAN (by Pepe Berba)](https://towardsdatascience.com/understanding-hdbscan-and-density-based-clustering-121dbee1320e)
* [HDBSCAN (by John Healy)](https://www.youtube.com/watch?v=dGsxd67IFiU)
* [Self Organizing Maps (by Abhinav Ralhan)](https://towardsdatascience.com/self-organizing-maps-ff5853a118d4)
[Back to Top](#introduction)
## Bayesian Inference
* [Markov Chain Monte Carlo (by Ben Shaver)](https://towardsdatascience.com/a-zero-math-introduction-to-markov-chain-monte-carlo-methods-dcba889e0c50)
* [Bayesian Network (by Gönül Aycı)](https://medium.com/@aycignl/bayesian-networks-bns-bc53b29c3f66)
[Back to Top](#introduction)
## Frequent Pattern Mining
* [Apriori Algorithm (Dimuth Tharaka Menikgama)](https://medium.com/@dimuthcse/apriori-algorithm-for-frequent-pattern-mining-7e8fb20b6aff)
[Back to Top](#introduction)
## Interpretable Machine Learning
* [Local Interpretable Model-agnostic Explanations (HackMD)](https://hackmd.io/aK6eDLAbRPGnjqrhicBLSQ)
* [Local Interpretable Model-agnostic Explanations (O'REILLY Media)](https://www.oreilly.com/learning/introduction-to-local-interpretable-model-agnostic-explanations-lime)
[Back to Top](#introduction)
## Online Courses
* [Mining of Massive Datasets (Stanford)](https://www.youtube.com/watch?v=xoA5v9AO7S0&list=PLLssT5z_DsK9JDLcT8T62VtzwyW9LNepV&ab_channel=ArtificialIntelligence-AllinOne)
* [Deep Learning with PyTorch (NYU)](https://www.youtube.com/watch?v=0bMe_vCZo30&list=PLLHTzKZzVU9eaEyErdV26ikyolxOsz6mq&ab_channel=AlfredoCanziani)
[Back to Top](#introduction)