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eriklindernoren/ML-From-Scratch

6061

eriklindernoren / ML-From-Scratch

Python

Bare bones Python implementations of some of the fundamental Machine Learning models and algorithms.


READ ME

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 way. The reason the project uses scikit-learn is to evaluate the implementations on sklearn.datasets.

Feel free to reach out if you can think of ways to expand this project.

Table of Contents

Usage

Installation

$ python setup.py install

Regression

$ python mlfromscratch/supervised_learning/regression.py

Figure: Polynomial ridge regression of temperature data measured in
Linköping, Sweden 2016.

Classification

$ python mlfromscratch/supervised_learning/multilayer_perceptron.py

Figure: Classification of the digit dataset using an MLP with two
hidden layers.

Clustering

$ python mlfromscratch/unsupervised_learning/dbscan.py

Figure: Clustering of the moons dataset using DBSCAN.

Association Analysis

$ python mlfromscratch/unsupervised_learning/apriori.py 
+-------------+
|   Apriori   |
+-------------+
Minimum Support: 0.25
Minimum Confidence: 0.8
Transactions:
    [1, 2, 3, 4]
    [1, 2, 4]
    [1, 2]
    [2, 3, 4]
    [2, 3]
    [3, 4]
    [2, 4]
Frequent Itemsets:
    [1, 2, 3, 4, [1, 2], [1, 4], [2, 3], [2, 4], [3, 4], [1, 2, 4], [2, 3, 4]]
Rules:
    1 -> 2 (support: 0.43, confidence: 1.0)
    4 -> 2 (support: 0.57, confidence: 0.8)
    [1, 4] -> 2 (support: 0.29, confidence: 1.0)

Current Implementations

Supervised Learning

Unsupervised Learning