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Machine Learning From Scratch. Bare bones NumPy implementations of machine learning models and algorithms with a focus on accessibility. Aims to cover everything from linear regression to deep learning.
| Date | Stars |
|---|---|
| 2026-07-31 | 32390 |
| 2026-08-01 | 32390 |
| 2026-08-03 | 32409 |
| 2026-08-06 | 32428 |
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# Machine Learning From Scratch
## About
Python implementations of some of the fundamental Machine Learning models and algorithms from scratch.
The purpose of this project is not to produce as optimized and computationally efficient algorithms as possible
but rather to present the inner workings of them in a transparent and accessible way.
## Table of Contents
- [Machine Learning From Scratch](#machine-learning-from-scratch)
* [About](#about)
* [Table of Contents](#table-of-contents)
* [Installation](#installation)
* [Examples](#examples)
+ [Polynomial Regression](#polynomial-regression)
+ [Classification With CNN](#classification-with-cnn)
+ [Density-Based Clustering](#density-based-clustering)
+ [Generating Handwritten Digits](#generating-handwritten-digits)
+ [Deep Reinforcement Learning](#deep-reinforcement-learning)
+ [Image Reconstruction With RBM](#image-reconstruction-with-rbm)
+ [Evolutionary Evolved Neural Network](#evolutionary-evolved-neural-network)
+ [Genetic Algorithm](#genetic-algorithm)
+ [Association Analysis](#association-analysis)
* [Implementations](#implementations)
+ [Supervised Learning](#supervised-learning)
+ [Unsupervised Learning](#unsupervised-learning)
+ [Reinforcement Learning](#reinforcement-learning)
+ [Deep Learning](#deep-learning)
* [Contact](#contact)
## Installation
$ git clone https://github.com/eriklindernoren/ML-From-Scratch
$ cd ML-From-Scratch
$ python setup.py install
## Examples
### Polynomial Regression
$ python mlfromscratch/examples/polynomial_regression.py
<p align="center">
<img src="http://eriklindernoren.se/images/p_reg.gif" width="640"\>
</p>
<p align="center">
Figure: Training progress of a regularized polynomial regression model fitting <br>
temperature data measured in Linköping, Sweden 2016.
</p>
### Classification With CNN
$ python mlfromscratch/examples/convolutional_neural_network.py
+---------+
| ConvNet |
+---------+
Input Shape: (1, 8, 8)
+----------------------+------------+--------------+
| Layer Type | Parameters | Output Shape |
+----------------------+------------+--------------+
| Conv2D | 160 | (16, 8, 8) |
| Activation (ReLU) | 0 | (16, 8, 8) |
| Dropout | 0 | (16, 8, 8) |
| BatchNormalization | 2048 | (16, 8, 8) |
| Conv2D | 4640 | (32, 8, 8) |
| Activation (ReLU) | 0 | (32, 8, 8) |
| Dropout | 0 | (32, 8, 8) |
| BatchNormalization | 4096 | (32, 8, 8) |
| Flatten | 0 | (2048,) |
| Dense | 524544 | (256,) |
| Activation (ReLU) | 0 | (256,) |
| Dropout | 0 | (256,) |
| BatchNormalization | 512 | (256,) |
| Dense | 2570 | (10,) |
| Activation (Softmax) | 0 | (10,) |
+----------------------+------------+--------------+
Total Parameters: 538570
Training: 100% [------------------------------------------------------------------------] Time: 0:01:55
Accuracy: 0.987465181058
<p align="center">
<img src="http://eriklindernoren.se/images/mlfs_cnn1.png" width="640">
</p>
<p align="center">
Figure: Classification of the digit dataset using CNN.
</p>
### Density-Based Clustering
$ python mlfromscratch/examples/dbscan.py
<p align="center">
<img src="http://eriklindernoren.se/images/mlfs_dbscan.png" width="640">
</p>
<p align="center">
Figure: Clustering of the moons dataset using DBSCAN.
</p>
### Generating Handwritten Digits
$ python mlfromscratch/unsupervised_learning/generative_adversarial_network.py
+-----------+
| Generator |
+-----------+
Input Shape: (100,)
+------------------------+------------+-------------Excerpt of 13,779 characters
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Would you bet a product on this? Bounded 0–100 and slow moving.
matched fp:065ee747e0bf96a4, llm:Repository description, topics (machine-learning-from-scratch, machine-learning, deep-learning, deep-reinforcement-learning, genetic-algorithm) and README stating it implements ML models and algorithms from linear regression to deep learning for educational purposes.