Python programming assignments for Machine Learning by Prof. Andrew Ng in Coursera

❄️ 🐳 Awesome tools and libs for AI, Deep Learning, Machine Learning, Computer Vision, Data Science, Data Analytics and Cognitive Computing that are baked in the oven to be Native on Kubernetes and Docker with Python, R, Scala, Java, C#, Go, Julia, C++ etc

A machine learning library for Python and Rust, for PyTorch, Tensorflow and SKLearn models

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

Collection of Notebooks from 30 Days of ML Challenge

Learn-python is a comprehensive repository designed to cover various aspects of Python learning. It includes examples, tutorials, and exercises to help beginners and developers enhance their Python programming skills. Perfect for anyone looking to start or advance their Python journey.

This repository is a long-term, evolving project focused on implementing Machine Learning algorithms from scratch and with optimized/library-based approaches side by side.

This project aims to forecast inflation rates using machine learning techniques. Leveraging historical economic data on GDP and unemployment rates, the model provides predictions for future inflation trends.

AI-powered Resume Optimization & Job Matching Platform. Analyzes job descriptions, detects ATS keyword gaps, rewrites resume bullet points using DeepSeek V3, and exports print-ready PDFs.

Crops disease detection mainly for tomato plants

❄️ 🐳 Awesome tools and libs for AI, Deep Learning, Machine Learning, Computer Vision, Data Science, Data Analytics and Cognitive Computing that are baked in the oven to be Native on Kubernetes and Docker with Python, R, Scala, Java, C#, Go, Julia, C++, Cognonic, etc.

This repository contains a complete, runnable example showing how Customer Success teams can use Logistic Regression to predict churn probability and act early.

This workspace contains a trained MobileNetV2 model for bird-species classification, the notebooks used to train and test it, and the dataset layout used for training/validation/testing.

Recover Gaussian-blurred BIP-39 seed phrases with CNN classification. 100% at mild blur, 93% at heavy.

This repository explores statistical and machine learning methods for classification and function approximation. It covers k-NN, Decision Trees, Information Theory, and Convexity, with Python implementations, theoretical explanations, model comparisons, and visualizations, making it a valuable resource for ML research and applications.

https://www.linkedin.com/in/sarbah-fred-junior

Stock predictions via ML - live long and prosper

AI and machine learning portfolio presenting projects in deep learning, computer vision, and intelligent systems.

Analyse et Prédiction des Prix de l'Immobilier en Californie

Machine Learning coursework covering financial distress classification and diabetes progression regression, with full Jupyter notebooks and reports.