> For the complete documentation index, see [llms.txt](https://digitalgarden.batamladen.com/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://digitalgarden.batamladen.com/notes/machine-learning.md).

# MACHINE LEARNING

- [Learning Algorythm Types](https://digitalgarden.batamladen.com/notes/machine-learning/learning-algorythm-types.md)
- [Supervised ML](https://digitalgarden.batamladen.com/notes/machine-learning/learning-algorythm-types/supervised-ml.md)
- [Linear Regression](https://digitalgarden.batamladen.com/notes/machine-learning/learning-algorythm-types/supervised-ml/linear-regression.md)
- [KNN](https://digitalgarden.batamladen.com/notes/machine-learning/learning-algorythm-types/supervised-ml/knn.md)
- [Decision Tree](https://digitalgarden.batamladen.com/notes/machine-learning/learning-algorythm-types/supervised-ml/decision-tree.md)
- [Random Forest](https://digitalgarden.batamladen.com/notes/machine-learning/learning-algorythm-types/supervised-ml/random-forest.md)
- [Support Vector Machine](https://digitalgarden.batamladen.com/notes/machine-learning/learning-algorythm-types/supervised-ml/support-vector-machine.md): Feature: supervised ; Common use: classification ; Output: class label ; Key Parameters: C(regularization), kernel, gamma
- [SVC](https://digitalgarden.batamladen.com/notes/machine-learning/learning-algorythm-types/supervised-ml/support-vector-machine/svc.md)
- [Linear Kernel](https://digitalgarden.batamladen.com/notes/machine-learning/learning-algorythm-types/supervised-ml/support-vector-machine/svc/linear-kernel.md)
- [Polynomial kernel](https://digitalgarden.batamladen.com/notes/machine-learning/learning-algorythm-types/supervised-ml/support-vector-machine/svc/polynomial-kernel.md)
- [RBF kernel](https://digitalgarden.batamladen.com/notes/machine-learning/learning-algorythm-types/supervised-ml/support-vector-machine/svc/rbf-kernel.md)
- [Unsupervised ML](https://digitalgarden.batamladen.com/notes/machine-learning/learning-algorythm-types/unsupervised-ml.md)
- [Isolation Forest](https://digitalgarden.batamladen.com/notes/machine-learning/learning-algorythm-types/unsupervised-ml/isolation-forest.md): Anomaly Detection Algorithm
- [DBscan](https://digitalgarden.batamladen.com/notes/machine-learning/learning-algorythm-types/unsupervised-ml/dbscan.md): Clustering Algorithm
- [Data Exploration](https://digitalgarden.batamladen.com/notes/machine-learning/data-exploration.md)
- [Data Pre-Processing](https://digitalgarden.batamladen.com/notes/machine-learning/data-pre-processing.md)
- [Training & Testing Data](https://digitalgarden.batamladen.com/notes/machine-learning/training-and-testing-data.md)
- [Train-Test Split](https://digitalgarden.batamladen.com/notes/machine-learning/training-and-testing-data/train-test-split.md)
- [Cross Validation](https://digitalgarden.batamladen.com/notes/machine-learning/training-and-testing-data/cross-validation.md)
- [K-Fold Cross Val](https://digitalgarden.batamladen.com/notes/machine-learning/training-and-testing-data/cross-validation/k-fold-cross-val.md)
- [Feature engineering](https://digitalgarden.batamladen.com/notes/machine-learning/feature-engineering.md)
- [Feeature Selection](https://digitalgarden.batamladen.com/notes/machine-learning/feature-engineering/feeature-selection.md)
- [SelectKBest](https://digitalgarden.batamladen.com/notes/machine-learning/feature-engineering/feeature-selection/selectkbest.md)
- [Feature extraction](https://digitalgarden.batamladen.com/notes/machine-learning/feature-engineering/feature-extraction.md)
- [TF-IDF](https://digitalgarden.batamladen.com/notes/machine-learning/feature-engineering/feature-extraction/tf-idf.md)
- [Feature Scaling](https://digitalgarden.batamladen.com/notes/machine-learning/feature-engineering/feature-scaling.md)
- [Normalization](https://digitalgarden.batamladen.com/notes/machine-learning/feature-engineering/feature-scaling/normalization.md)
- [Standardization](https://digitalgarden.batamladen.com/notes/machine-learning/feature-engineering/feature-scaling/standardization.md)
- [Standard Scaler](https://digitalgarden.batamladen.com/notes/machine-learning/feature-engineering/feature-scaling/standardization/standard-scaler.md)
- [Normalization vs Standardizatio](https://digitalgarden.batamladen.com/notes/machine-learning/feature-engineering/feature-scaling/normalization-vs-standardizatio.md)
- [ML techniques](https://digitalgarden.batamladen.com/notes/machine-learning/ml-techniques.md)
- [Anomaly Detection](https://digitalgarden.batamladen.com/notes/machine-learning/anomaly-detection.md)
- [Classification Raport](https://digitalgarden.batamladen.com/notes/machine-learning/classification-raport.md)


---

# Agent Instructions
This documentation is published with GitBook. GitBook is the documentation platform designed so that both humans and AI agents can read, navigate, and reason over technical content effectively. Learn more at gitbook.com.

## Querying This Documentation
If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question.

Perform an HTTP GET request on the current page URL with the `ask` query parameter, and the optional `goal` query parameter:

```
GET https://digitalgarden.batamladen.com/notes/machine-learning.md?ask=<question>&goal=<endgoal>
```

`ask` is the immediate question: it should be specific, self-contained, and written in natural language.
`goal` is optional and describes the broader end goal you are ultimately trying to accomplish on behalf of the user. GitBook uses it to tailor the answer towards what is most useful for that goal.

The response will contain a direct answer to the question and relevant excerpts and sources from the documentation.

Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.