🧠 Made of Code: Machine Learning Regression with Gradient Descent and Stochastic Optimization

Large Scale Machine/Deep Learning library for Python

How to build a simple neural network from scratch using Numpy and linear algebra without relying on high-level libraries like TensorFlow or Keras.

Animating how Adaline classification works by minimizing cost. Showing comparison of three kinds of gradient descent.

Generalized local search tool

Linear regression and Normal equation implementation of predicting the life expectancies in different countries.

Practice on ML Specialization by Deeplearning.ai

Rust implementation of the Adaline artificial neural network algorithm for educational purposes.

Step-by-Step Guide to an Optimization Problem Solver in Scala

Predicting House Sale Prices with Machine Learning

Uses Matrix factorisation on a sparse matrix to predict the missing values of rating of movies by users using stochastic and batch gradient descent.

This repository contains numpy implementations of different ML Algorithms

Multiclass Logistic, Classification Pipeline, Cross Validation, Gradient Descent, Regularization

Logistic regression model for breast cancer prediction using imaging features, achieving 98.24% accuracy on Kaggle dataset without vectorization.