Machine Learning Implementations in Python

This repository contains different machine learning algorithm implementations, inspired from resources. It is using Jupyter notebooks and JupyterLab.

Usage

Install dependencies

poetry update

Start JupyterLab

poetry run jupyter lab

or

poetry shell
jupyter lab

Implemented Algorithms

• Comparison of various classification models from scikit-learn: model_comparison.ipynb
• Optimization and root finding: optimization.ipynb
  • Gradient Descent
  • Bisection Method
  • Secant Method
  • Newton's Method
• Linear Regression: linear_regression.ipynb
• K-Means: kmeans.ipynb
• K-Nearest Neighbors: knn.ipynb
• Naive Bayes: naive_bayes.ipynb
• Perceptron: perceptron.ipynb
• Backpropagation: backpropagation.ipynb
• Decision Tree: decision_tree.ipynb
• Logistic Regression: logistic_regression.ipynb
• Feedforward Neural Network: feedforward.ipynb
• Support Vector Machine: svm.ipynb

Links & Resources

• scikit-learn machine learning library for Python
• An Introduction to Statistical Learning
• The Elements of Statistical Learning
• Introduction to Computation and Programming Using Python
• Algorithms From Scratch
• Kaggle
• UCI Machine Learning Resources

Package Management

This project is using poetry Python package and dependency manager.

• Init interactively poetry init
• Add package poetry add package-name
• Remove package poetry remove package-name
• Install dependencies poetry install
• Update dependencies poetry update
• Show available packages poetry show
• Run a command in the virtualenv poetry run command
• Open virtualenv poetry shell