{"repo":"trekhleb/homemade-machine-learning","free":true,"listed":false,"github":"https://github.com/trekhleb/homemade-machine-learning","clone":"git clone https://github.com/trekhleb/homemade-machine-learning.git","description":"🤖 Python examples of popular machine learning algorithms with interactive Jupyter demos and math being explained","language":"Jupyter Notebook","stars":24766,"topics":["python","jupyter","jupyter-notebook","machine-learning","machinelearning","machine-learning-algorithms","algorithm"],"license":"MIT","category":"machine-learning","readme_excerpt":"Homemade Machine Learning 🇺🇦 UKRAINE IS BEING ATTACKED BY RUSSIAN ARMY. CIVILIANS ARE GETTING KILLED. RESIDENTIAL AREAS ARE GETTING BOMBED. - Help Ukraine via: - Serhiy Prytula Charity Foundation - Come Back Alive Charity Foundation - National Bank of Ukraine - More info on war.ukraine.ua and MFA of Ukraine Read this in other languages: Español You might be interested in: - Homemade GPT • JS - Interactive Machine Learning Experiments For Octave/MatLab version of this repository please check machine-learning-octave project. This repository contains examples of popular machine learning algorithms implemented in Python with mathematics behind them being explained. Each algorithm has interactive Jupyter Notebook demo that allows you to play with training data, algorithms configurations and immediately see the results, charts and predictions right in your browser . In most cases the explanations are based on this great machine learning course by Andrew Ng. The purpose of this repository is not to implement machine learning algorithms by using 3 rd party library one-liners but rather to practice implementing these algorithms from scratch and get better understanding of the mathematics behind each algorithm. That's why all algorithms implementations are called \"homemade\" and not intended to be used for production. Supervised Learning In supervised learning we have a set of training data as an input and a set of labels or \"correct answers\" for each training set as an output. Then w","default_branch":null,"files":null,"tree":[],"storefront":"/r/trekhleb","claimed":false,"request_supported":{"post":"https://gitbuyer.com/r/trekhleb/homemade-machine-learning/request-supported","requests":0},"note":"indexed from public GitHub; nothing is for sale on this page. Clone it from GitHub. Paid listings live at /search."}