Jupyter Notebooks from the old UnsupervisedLearning.com (RIP) machine learning and statistics blog
Awesome list (courses, books, videos etc.) and implementation of Machine Learning Algorithms
Multi-class malware classification using Deep Learning
Amazon Rekognition Code Samples
Решение задач различных Яндекс-контестов
Проекты курса Аналитик данных (Яндекс.Практикум)
An extensive collection of data and artificial intelligence (AI) notebook templates, curated to encompass models, analytics, code snippets, and a variety of resources. ~ Team ML Nagpur
Machine learning methods for identifing investment factors
Webseeded torrent creator using Google Colaboratory
A Python library to program a Luos based network through a high level interface.
The climbing crux model is a machine-learning project that aims to recognize climbing holds and the distance between them from a photo and suggest routes that fit the user's climbing level.
This repository contains all the material for the DEC Python training. This training is developed by DIME and DECID.
Use Watson Studio and PyTorch to create a machine learning model to recognize hand-written digits
Explore, analyse and visualise Betfair Historical Data Feed using PySpark.
Launch a Jupyter Notebook using the Ape Framework
Implementation of an Alzheimer's Disease detection system using Deep Learning on MRI images from a Kaggle Dataset.
This project is developed in Python and it proposes the development of a Bayesan Network to infer the probabilities of serious floods in the territory of the Italian region Veneto.
Your ultimate hub for JavaScript exploration and mastery, featuring a diverse collection of meticulously curated code snippets and projects.
Real-Time Emotion Detection using MobileNetV2 is a deep learning project that detects and classifies human emotions in real time via webcam. It uses a MobileNetV2 model trained on facial expressions and OpenCV for face detection to recognize emotions like Happy, Sad, Angry, and more.
A Streamlit App for Customer Segmentation Project using Kmeans Clustering (Best Choice)
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