https://github.com/finite-sample/ds
Learning From Data
https://github.com/finite-sample/ds
data-science statistics
Last synced: 3 months ago
JSON representation
Learning From Data
- Host: GitHub
- URL: https://github.com/finite-sample/ds
- Owner: finite-sample
- Created: 2018-07-23T20:50:32.000Z (almost 8 years ago)
- Default Branch: master
- Last Pushed: 2022-08-30T19:50:15.000Z (almost 4 years ago)
- Last Synced: 2026-01-11T08:42:31.954Z (5 months ago)
- Topics: data-science, statistics
- Language: HTML
- Size: 34.7 MB
- Stars: 2
- Watchers: 1
- 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