Machine learning is the practice of teaching a computer to learn. The concept uses pattern recognition, as well as other forms of predictive algorithms, to make judgments on incoming data. This field is closely related to artificial intelligence and computational statistics.
12 weeks, 26 lessons, 52 quizzes, classic Machine Learning for all
Convert documents to structured data effortlessly. Unstructured is open-source ETL solution for transforming complex documents into clean, structured formats for language models. Visit our website to learn more about our enterprise grade Platform product for production grade workflows, partitioning, enrichments, chunking and embedding.
A massive list including a huge amount of products and services that are completely free!
Slides, paper notes, class notes, blog posts, and research on ML 📉, statistics 📊, and AI 🤖.
Toolkit for Apache Spark ML for Feature clean-up, feature Importance calculation suite, Information Gain selection, Distributed SMOTE, Model selection and training, Hyper parameter optimization and selection, Model interprability.
This is a AI Based Smart Exam Proctoring System using python flask, mysql as database, yolov4
A platform of machine learning based website that recommends the best crop to grow, fertilizers to use, and the diseases caught by your crops.
Stress classifier with AutoML
Reading notes on deep learning papers---深度学习论文阅读笔记 (2013-2018)
To have a proper posture🪑, you need to stay upright while sitting. This website uses machine learning model to learn patterns 👣 in your posture and helps your back by keeping you in a proper posture.
Upload group photos, arrange them by faces using ML and share them with ease. 🤳🤖
GigaPack is API and calculation layer based on AlgoPack from MOEX
An analysis of the systemic impact, on the Web, of AI systems, and in particular ones based on Machine Learning models, and the role that Web standardization may play in managing that impact
CropCareAI is an AI-powered web application built using Flask to assist plant enthusiasts, farmers, and researchers in identifying and diagnosing plant diseases using pretrained Machine Learning models.
This repository demonstrates browser based implementation of DepthAnything and DepthAnythingV2 models. It is powered by Onnx and does not require any web servers or APIs.