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https://github.com/jabhij/datascience_knowhow

This repository is dedicated to all the important and insightful resources dedicated to Data Science, Machine Learning (ML), Artificial Intelligence (AI) and all the other related domains. Feel free to come along to share your thoughts, & reference materials.
https://github.com/jabhij/datascience_knowhow

Last synced: about 23 hours ago
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Host: GitHub
URL: https://github.com/jabhij/datascience_knowhow
Owner: jabhij
Created: 2023-02-04T05:43:59.000Z (almost 2 years ago)
Default Branch: main
Last Pushed: 2024-04-13T18:38:44.000Z (7 months ago)
Last Synced: 2024-04-14T08:54:23.915Z (7 months ago)
Language: MATLAB
Homepage:
Size: 1.77 MB
Stars: 1
Watchers: 2
Forks: 1
Open Issues: 0
Metadata Files:
- Readme: README.md

Awesome Lists containing this project

README

        ## Data Science Know-How?

This repository is dedicated to all the important and insightful resources dedicated to Data Science, Machine Learning (ML), 

Artificial Intelligence (AI), and all the other related domains. Feel free to come along to share your thoughts and reference material.

**[A]**

  - [A/B Testing - YT](https://www.youtube.com/watch?v=X8u6kr4fxXc)

  - [AUC - TDS](https://towardsdatascience.com/understanding-auc-roc-curve-68b2303cc9c5)

**[B]**

  - **Bayes Theorem**

    - [Bayes Theorem](https://www.freecodecamp.org/news/bayes-rule-explained/)

    - [Bayes Theorem - SQ](https://www.youtube.com/watch?v=9wCnvr7Xw4E)

  

  - [Bagging](https://tinyurl.com/2p9fsjnm)

  - **Bias-Variance Tradeoff**

    - [Bias-Variance Tradeoff - SQT](https://www.youtube.com/watch?v=EuBBz3bI-aA)

    - [Bias Variance Tradeoff - TDS](https://towardsdatascience.com/understanding-the-bias-variance-tradeoff-165e6942b229)

**[C]**

  - [Conditional Probability](https://www.mathsisfun.com/data/probability-events-conditional.html)

  - [Confusion Matrix - TDS](https://towardsdatascience.com/understanding-confusion-matrix-a9ad42dcfd62)

  - [CNN]()

  

  - **Cross Validation**

    - [Cross Validation - TDS](https://towardsdatascience.com/what-is-cross-validation-60c01f9d9e75)

    - [Cross Validation - KN](https://www.youtube.com/watch?v=7062skdX05Y)

   

  - [Cumulative Distribution Function]()

**[D]**

  - **Decision Trees - Stat Quest**

    - [Classification Decision Trees](https://www.youtube.com/watch?v=_L39rN6gz7Y)

    - [Regression - Decision Trees](https://www.youtube.com/watch?v=g9c66TUylZ4)

    - [Tree Pruning - Cost Complexity Pruning](https://www.youtube.com/watch?v=D0efHEJsfHo)

  - [Degree of Freedom - SHT](https://www.statisticshowto.com/probability-and-statistics/hypothesis-testing/degrees-of-freedom/)

      

**[E]**

  - [EDA]()

  - [Encoding]()

  - [Essential Statistics - LDS](https://www.learndatasci.com/tutorials/data-science-statistics-using-python/)

  - [ETL]()

**[G]**

  - [Gaussian Mixture Model - TDS](https://towardsdatascience.com/gaussian-mixture-models-explained-6986aaf5a95)

**[H]**

  - **Hypothesis Testing**

    - [Hypothesis Testing - SHT](https://www.statisticshowto.com/probability-and-statistics/hypothesis-testing/)

    - [Hypothesis Testing - SQ](https://www.youtube.com/watch?v=0oc49DyA3hU)

    

**[I]**

  - [IQR - SHT](https://www.statisticshowto.com/probability-and-statistics/interquartile-range/)

**[K]**

  - [KNN - DC](https://www.datacamp.com/tutorial/k-nearest-neighbor-classification-scikit-learn)

**[L]**

  - **Lasso & Ridge Regression**

    - [Lasso & Ridge Regression - DC](https://www.datacamp.com/tutorial/tutorial-lasso-ridge-regression)

    - [Lasso & Ridge Regression - KN](https://www.youtube.com/watch?v=9lRv01HDU0s)

    - [Lasso & Ridge Regression - KN](https://www.youtube.com/watch?v=vaQxdBEcBzU)

    

  - [Learning Rate - TDS](https://towardsdatascience.com/understanding-learning-rates-and-how-it-improves-performance-in-deep-learning-d0d4059c1c10)

  - [Linear Discriminant Analysis](https://towardsdatascience.com/linear-discriminant-analysis-explained-f88be6c1e00b)

  

  - **Linear Regression**

    - [Linear Regression](https://www.youtube.com/watch?v=zD-zN6VkX-A&list=PLJDUkOtqDm6UeH59-jG31Cma-abXLNse_&index=2)

    - [Linear Regression - LDS](https://www.learndatasci.com/tutorials/predicting-housing-prices-linear-regression-using-python-pandas-statsmodels/)

  

  - **Logistic Regression**

    - [Logistic Regression](https://www.datacamp.com/tutorial/logistic-regression-R)

    - [Logistic Regression - KN](https://www.youtube.com/watch?v=L_xBe7MbPwk)

    - [Logistic Regression - KN](https://www.youtube.com/watch?v=uFfsSgQgerw)

    - [Logistic Regression (MultiClass) - KN](https://www.youtube.com/watch?v=V8fS0T_ktn4)

    - [Logistic Regression - SQ](https://www.youtube.com/watch?v=yIYKR4sgzI8&list=PLblh5JKOoLUKxzEP5HA2d-Li7IJkHfXSe)

  

**[M]**

  - **Maximum Likelihood Estimation**

    - [Maximum Liklihood Estimation](https://bit.ly/3JFHxGt)

    - [Maximum Likelihood Estimation - SQ](https://www.youtube.com/watch?v=BfKanl1aSG0&list=PLblh5JKOoLUKxzEP5HA2d-Li7IJkHfXSe&index=3)

    

  - [Mean Square Error - TDS](https://bit.ly/3wSjYmj)

  - [Machine Learning Types](https://shorturl.at/gyCMZ)

  

  - **Monte Carlo**

     - [Monte Carlo Accept–Reject Method](https://panjeh.medium.com/monte-carlo-accept-reject-method-cb6cc2a76840#7f85)

   

  - [Multilinear Regression]()

**[N]**

  - **Naive Bayes**

    - [Naive Bayes Algorithm -KDNuggets](https://www.kdnuggets.com/2020/06/naive-bayes-algorithm-everything.html)

    - [Naive Bayes - SQ](https://www.youtube.com/watch?v=O2L2Uv9pdDA)

  - [Neural Networks]()

**[P]**

  - **P-Value**

    - [P-Value - SHT](https://www.statisticshowto.com/probability-and-statistics/statistics-definitions/p-value/)

    - [P-Value - SQ](https://www.youtube.com/watch?v=vemZtEM63GY)

      

  - **Probability Distribution Functions**

    - [Statistical Distributions - SHT](https://www.statisticshowto.com/probability-and-statistics/statistics-definitions/probability-distribution/)

    - [Probability Distribution Functions](https://www.analyticsvidhya.com/blog/2021/07/probability-types-of-probability-distribution-functions/)

    - [Bernoulli Distribution](https://www.statisticshowto.com/probability-and-statistics/statistics-definitions/probability-distribution/bernoulli-distribution/)

    - [Binomial Distribution]()

    - [Poisson Distribution]()

    - [Chi-Squared Distribution]()

    - [Normal distribution]()

   

  - [PCA]()

**[R]**

  - [Random Forest - SQ]()

  - **Regularization**

    - [Regularization (L2) - SQ](https://www.youtube.com/watch?v=Q81RR3yKn30)

    - [Regularization - SHT](https://www.statisticshowto.com/regularization/)

    - [Regularization (L1 & L2) - KN](https://www.youtube.com/watch?v=9lRv01HDU0s)

  

  - [ROC -  TDS](https://towardsdatascience.com/understanding-auc-roc-curve-68b2303cc9c5)

  - [Row Level Security - Microsoft](https://learn.microsoft.com/en-us/power-bi/enterprise/service-admin-rls)

  - [R-Square - SQ](https://www.youtube.com/watch?v=xxFYro8QuXA&list=PLblh5JKOoLUKxzEP5HA2d-Li7IJkHfXSe&index=5)

  - [Residual Standard Error]()

**[S]**

  - [Statistical Hypothesis](https://shorturl.at/stwJM)

  - [Sum of Square: SST, SSR, SSE](https://365datascience.com/tutorials/statistics-tutorials/sum-squares/)

  - [Supervised Machine Learning]()

  - [SVM - TDS](https://towardsdatascience.com/support-vector-machine-introduction-to-machine-learning-algorithms-934a444fca47)

**[T]**

  - [Test Statistics - CC](https://www.youtube.com/watch?v=QZ7kgmhdIwA)

  - [T-Test - SHT](https://www.statisticshowto.com/probability-and-statistics/t-test/)

  - [T-Distribution - SHT](https://www.statisticshowto.com/probability-and-statistics/t-distribution/)

  - [TSS - SHT](https://www.statisticshowto.com/residual-sum-squares/)

  - [Type - I & II Errors - Scribbr](https://www.scribbr.com/statistics/type-i-and-type-ii-errors/)

**[U]**

  - [Underfitting & Overfitting - TDS](https://towardsdatascience.com/overfitting-and-underfitting-principles-ea8964d9c45c)

  - [Unsupervised Machine Learning]()

**[V]**

  - [Vanishing Gradient Problem]()

**[Z]**

  - [Z-Statistic - SHT](https://www.statisticshowto.com/probability-and-statistics/z-score/)

**[]**