https://github.com/soodoku/ds

Learning From Data
https://github.com/soodoku/ds

data-science statistics

Last synced: 22 days ago
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Learning From Data

• Host: GitHub
• URL: https://github.com/soodoku/ds
• Owner: soodoku
• Created: 2018-07-23T20:50:32.000Z (over 6 years ago)
• Default Branch: master
• Last Pushed: 2022-08-30T19:50:15.000Z (over 2 years ago)
• Last Synced: 2024-10-11T12:18:10.074Z (3 months ago)
• Topics: data-science, statistics
• Language: HTML
• Size: 34.7 MB
• Stars: 2
• Watchers: 2
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

Awesome Lists containing this project

README

        
## Learning From Data

* [Basics of Data Science](01_stat/)

    - When is the *mean* useful?

    - Correlation---intuition and problems

* [Cost](02_cost/)

    - Functional Estimation as Cost Minimization

    - What's the cost?

    - How to minimize costs

    - Derivation of simple linear regression

* [Error, Bias, Variance](03_bias_variance/)

    - Decomposing Error in Bias, Variance, Irreducible Error

    - Practical Trade-offs between Bias and Variance

    - Regularization, Dropout, etc.

* [Evaluating Models](04_eval/)

    - Metrics for evaluating models

    - Clearing up confusion about confusion matrices

    - How to construct your test data

* [What Data to Collect?](05_what_data_to_collect/)

    - Active learning

    - Generating types

* [Causal Inference](06_causal_inf/)

    - *Cosal* Inference

    - Experimental Inference

    - Power

* [Fair ML](08_fair_ml/)

    - Concerns

    - Solutions

* [Interpretable ML](09_interpretable_ml/)

    - Why?

    - How?

    - Concerns

* [Problem Solving With Data](10_psd/)

    - Why?

    - How?

    - Concerns

* [Model Testing](11_model_testing/)

    - Why cross-validation isn't enough

    - Lessons from SWE

### Author

Gaurav Sood