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The first machine learning framework that encourages learning ML concepts instead of memorizing class functions.
| Date | Stars |
|---|---|
| 2026-07-31 | 340 |
| 2026-08-02 | 340 |
| 2026-08-06 | 340 |
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<p align="center">
<img src="https://github.com/anish-lakkapragada/SeaLion/blob/main/logo.png?raw=true" width=300 height=300 >
</p>
# SeaLion
[](https://sealion.readthedocs.io/en/latest/?badge=latest)
SeaLion is designed to teach today's aspiring ml-engineers the popular
machine learning concepts of today in a way that gives both intuition and
ways of application. We do this through concise algorithms that do the job
in the least jargon possible and examples to guide you through every step
of the way.
## Quick Demo
<p align="center">
<img src="https://raw.githubusercontent.com/anish-lakkapragada/SeaLion/main/sealion_demo.gif" width = 580 height = 326>
<br>
<i>SeaLion in Action</i>
</p>
## General Usage
For most classifiers you can just do (we'll use Logistic Regression as an
example here) :
```python
from sealion.regression import LogisticRegression
log_reg = LogisticRegression()
```
to initialize, and then to train :
``` python
log_reg.fit(X_train, y_train)
```
and for testing :
```python
y_pred = log_reg.predict(X_test)
evaluation = log_reg.evaluate(X_test, y_test)
```
For the unsupervised clustering algorithms you may do :
```python
from sealion.unsupervised_clustering import KMeans
kmeans = KMeans(k = 3)
```
and then to fit and predict :
```python
predictions = kmeans.fit_predict(X)
```
Neural networks are a bit more complicated, so you may want to check an example
[here.](https://github.com/anish-lakkapragada/SeaLion/blob/main/examples/deep_learning_example.ipynb)
The syntax of the APIs was designed to be easy to use and familiar to most other ML libraries. This is to make sure both beginners and experts in the field
can comfortably use SeaLion. Of course, none of the source code uses other ML frameworks.
## Testimonials, Stats, and Reddit Posts
"Super Expansive Python ML Library"
- [@Dr. Peter Washington](https://twitter.com/peter\_washing/status/1356766327541616644)
[Analytics Vidhya calls SeaLion's algorithms **beginner-friendly**, **efficient**, and **concise**.](https://www.analyticsvidhya.com/blog/2021/02/6-open-source-data-science-projects-that-provide-an-edge-to-your-portfolio/)
Stats :
- [**2000+ downloads**](https://pypistats.org/packages/sealion)
- **300+ stars**
- **15,000 views on GitHub**
- **100+ forks/clones**
[r/learningmachinelearning Post](https://www.reddit.com/r/learnmachinelearning/comments/lfv72l/a_set_of_jupyter_notebooks_to_help_you_understand/)
## Installation
The package is available on PyPI. Install like such :
``` shell
pip install sealion
```
SeaLion can only support Python 3, so please make sure you are on the
newest version.
## General Information
SeaLion was built by Anish Lakkapragada, a freshman in high school, starting in Thanksgiving of 2020
and has continued onto early 2021. The library is meant for beginners to
use when solving the standard libraries like iris, breast cancer, swiss
roll, the moons dataset, MNIST, etc.
## Documentation
WE HAVE DOCS NOW! Thanks [@Patrick Huang](http://github.com/phuang1024)! All available here: [docs](https://sealion.readthedocs.io/).
Also, use the examples to your advantage!
### Updates for v4.1 and up!
First things first - thank you for all of the support. ThExcerpt of 11,392 characters
Read on GitHubWould you bet a product on this? Bounded 0–100 and slow moving.
matched fp:030d480d916d19ac, llm:Repository topics and description: 'machine-learning', 'neural-networks', 'kmeans-clustering', 'dbscan-clustering', 'k-nearest-neighbors', 'regression-algorithms'; README: 'machine learning framework... teach ML concepts', usage examples for classifiers and clustering.
matched fp:030d480d916d19ac, llm:Repository topics and description: 'machine-learning', 'neural-networks', 'kmeans-clustering', 'dbscan-clustering', 'k-nearest-neighbors', 'regression-algorithms'; README: 'machine learning framework... teach ML concepts', usage examples for classifiers and clustering.