Autonomous Vehicle modelling using MATLAB and Simulink

Implementation of soft dynamic time warping in pytorch

A dependency free library of standardized optimization test functions written in pure Python.

A very simple Genetic Algorithm implementation for matlab, easy to use, easy to modify runs fast.

No description provided.

🟣 Cost Function interview questions and answers to help you prepare for your next machine learning and data science interview in 2026.

Economic Dispatching by Bees Algorithm

This program implements logistic regression from scratch using the gradient descent algorithm in Python to predict whether customers will purchase a new car based on their age and salary.

building a deep neural network with as many layers as you want!

Here, we implement regularized linear regression to predict the amount of water flowing out of a dam using the change of water level in a reservoir. In the next half, we go through some diagnostics of debugging learning algorithms and examine the effects of bias v.s. variance.

This repository provides a comprehensive machine learning course with theoretical concepts and practical implementations

Implementation is to use gradient descent to find the optimal values of θ that minimize the cost function.

Neural networks is a model inspired by how the brain works. It is widely used today in many applications: when your phone interprets and understand your voice commands, it is likely that a neural network is helping to understand your speech; when you cash a check, the machines that automatically read the digits also use neural networks.

A from-scratch Linear Regression model optimized via Gradient Descent for house price prediction.

Presentations And Source Codes of My Machine Learning Mini Course

Implement a safe autonomous navigation in a simulated 3D environment full of cars. Apply concepts like prediction, finite state machines, behavior planning, and more.

Project for "Clustering" Master course of Data Science and Information Technologies (DSIT)

OpenLoss: This repository discloses cost functions designed for open-set classification tasks, namely, Entropic Open-set, ObjectoSphere and Maximal-Entropy Loss.

A Mathematical Intuition behind Linear Regression Algorithm

This repository consists of Lab Assignments for course Machine Learning.

Monte-Carlo search for the minimum of the multidimensional "cost" function

No description provided.

Machine Learning

Highway Driving (project 7 of 9 from Udacity Self-Driving Car Engineer Nanodegree)

Highway Path Planner

This project demonstrates how to build a Multiple Linear Regression model in pure Python without using any ML libraries like scikit-learn. It includes: Complete implementation of linear regression from the ground up Manual computation of the cost function (MSE) and gradien

Chapter Wise Notes and codes of Book "Python Machine Learning by Sebestian Raschka".

This Python repository contains an example of linear regression using a single independent variable to predict a continuous dependent variable. It includes code for importing and preprocessing the data, fitting the model, and evaluating its performance. The repo also includes plots of the fitted models and analysis of the results.

Implementing the gradient descent algorithm from scratch to perform univariate linear regression to analyze the profit made by a bike sharing company.

This repository contains the lab work of the course Machine Learning (IE 406).