Implementing machine learning algorithms from scratch.

The first machine learning framework that encourages learning ML concepts instead of memorizing class functions.

This repository explores the variety of techniques and algorithms commonly used in machine learning and the implementation in MATLAB and PYTHON.

Data science for beginners involves learning to extract insights from data using statistics, programming (Python/R), and visualization. Key steps include data collection, cleaning, analysis, modeling, and communicating findings. Beginners should start with Python, basic math (linear algebra/calculus), and build projects to create a portfolio.

Learn to build a basic machine learning model from scratch with this repo and tutorial series.

Estimated annual CO2 emissions from diesel generators at mobile or cell towers

Covid-19 Mu Variant [B.1.621] Prediction and Classification Artificial Intelligence/Machine Learning Software

Tesla Car Range, Base Price and Precise Price Prediction - Machine Learning

eXtreme MultiLabel Classification tutorial notebook for Machine Learners (with video)

Machine Learning with Python

Ozone Day AdaBoostClassifier and Random Forest Tree Classifier with Machine Learning

Dry Natural Gas Production Prediction of the United States

Machine Learning Algorithms implemented in Python

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

Cross-platform GUI tool to create and manage (machine learning) configurations and run them automatically and remotely.

All my learnings from "Machine Learning with Python" course offered by "IBM" on Coursera are reflected here.

An intermediate level course of machine learning offered by IBM on Cognitive Class.io

A machine learning library created in python by Okerew

Python code (including ipython notebook) for naive bayes classifier to classify salaries of adults based on various attributes

This module extends the kernel SHAP method (as introduced by Lundberg and Lee (2017)) which is local in nature, to a method that computes global SHAP values.

Going through the book "Python Machine Learning" by Sabastian Raschka and Vahid Mirjalili

Eroji is an elegant, modern web application that leverages OpenAI's GPT-4.1 Vision capabilities to provide detailed face analysis from uploaded images. The app features a polished, responsive UI built with Streamlit and custom CSS styling.

Machine learning models in NumPy

Generating code with Ludwig AI/ML (PyTorch, Tensorflow)

Another repository for trying out machine learning algorithms for classification

Detects suspicious web traffic using ML & Deep Learning (Random Forest, Neural Network, Isolation Forest)

Customer Churn Prediction using Machine Learning

🐦Tweet Sentiment Analyzer 📊

this are a different 6 Algorithm with different type of data that is good for practice

Image multiclass classification of astronomical objects using k-means clustering for feature generation and different supervised learning models to train and test.