ML-From-Scratch

GitHub describes it as 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.. The repository metadata lists Python as its primary language. The metadata lists the MIT license. This article stays within the project description and details documented in the GitHub repository README.

GitHub describes it as 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.. The repository metadata lists Python as its primary language. The metadata lists the MIT license. The README describes the project this way: Python implementations of some of the fundamental Machine Learning models and algorithms from scratch.

The README section "About" states: 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.

The README section "Installation" states: $ git clone https://github.com/eriklindernoren/ML-From-Scratch $ cd ML-From-Scratch $ python setup.py install

The README section "Polynomial Regression" states: Figure: Training progress of a regularized polynomial regression model fitting temperature data measured in Linköping, Sweden 2016.

The repository README is the source for this review. It does not replace a local installation or an independent test.

• Source 1 · Project repository
• Source 2 · README