persian Ham Spam detector with python, hazm, nltk and nlp.

Welcome to the official GitHub repository for the "Machine Learning" course 2024! In this course, we explore the fascinating world of machine learning, diving deep into the algorithms, techniques, and tools that enable computers to learn from data and make intelligent decisions.

This project involves cohort analysis and customer segmentation to help an e-commerce giant improve its product offerings, customer relations and maximize profit.

The goal of this assignment is to perform an in-depth analysis of a dataset containing information about cars and fuel consumption. We will use Python for the solution, leveraging libraries such as Pandas for data manipulation and analysis, and NumPy for numerical operations.

End-to-End Machine Learning/Data Science Project

this repository contains two projects : the first it s applying ML algorithm (Logistic regression) for classification on Titanic dataset From scratch and with use Sickit-Learn and the second for analyze this data : Understanding data - data preprocessing

The code below is for a machine learning project that builds a regression model using different algorithms and evaluates their performance.

L'objectif de cette étude est de définir à partir des enquêtes d'insertion professionnelles réalisé par le ministère de l'enseignement supérieur si le salaire moyen annuel brut permet lmoyen permet de faire partie des 30% des français les plus riches

Projeto final da disciplina de Machine Learning (2022.2).

The project involves analyzing transactions and classifying them into fraudulent and non-fraudulent through Machine Learning techniques.

Machine Learning & Deep learning

A very simple neural network implementation in python.

2022학년도 AI 인력양성 과정 (알고리즘으로 풀어보는 신경망, 딥러닝)

Implémentation d'une pipeline permettant de faire la prédiction de la maladie de parkinson via des outils d'IoT, Cloud, et Big Data

Titanic Kaggle Competition

a web application for spam emails detection

Laboratory with random forest, logistic regression and SVM. The dataset used for this test is a set of points generated randomly with the following specification: • Number of Samples: 1200 • Number of Classes: 3 • Number of Features: 2 (Length and Width).

This project aims to build an AI agent to recognize handwritten digits using machine learning algorithms. The agent will extract the handwritten text from an image and classify it into their respective digit labels using a machine learning module trained upon the MNIST dataset by the SVM algorithm.