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.

• Machine Learning From Scratch
  • About
  • Table of Contents
  • Usage
    • Installation
    • Classification
    • Clustering
    • Association Analysis
  • Current Implementations
    • Supervised Learning
    • Unsupervised Learning

$ pip install -r requirements.txt

$ python supervised_learning/multilayer_perceptron.py

Figure: Classification of the digit dataset using MLP.

$ python unsupervised_learning/dbscan.py

Figure: Clustering of the moons dataset using DBSCAN.

$ python unsupervised_learning/apriori.py 
- Apriori -
Minimum - support: 0.25, 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)

• Adaboost
• Decision Tree
• Gradient Boosting
• K Nearest Neighbors
• Linear Discriminant Analysis
• Linear Regression
• Logistic Regression
• Multi-class Linear Discriminant Analysis
• Multilayer Perceptron
• Naive Bayes
• Perceptron
• Random Forest
• Ridge Regression
• Support Vector Machine
• XGBoost

• Apriori
• DBSCAN
• FP-Growth
• Gaussian Mixture Model
• K-Means
• Partitioning Around Medoids
• Principal Component Analysis