DataAnalysisToolkit is a Python-based data analysis tool designed to streamline various data analysis tasks. It provides the ability to load data from CSV files, perform statistical calculations, detect outliers, clean data, and visualize data.

⏱ Benchmarks of machine learning inference for Go

Automatic paper clustering and search tool by fastext from Facebook Research

ONNX Runtime: cross-platform, high performance ML inferencing and training accelerator

In this article, I will be implementing a predictive model on Rain Dataset to predict whether or not it will rain tomorrow in Australia. The Dataset contains about 10 years of daily weather observations of different locations in Australia. By the end of this article, you will be able to build a predictive model.

A unified framework for machine learning with time series

skscope: Sparse-Constrained OPtimization via itErative-solvers

a streamlit appplication to assist with heart disease prediction, skin cancer and tubercolosis detection also with a health chatbot.

a streamlit application that detects emotions in real-time and also in images and videos.

Email spam filtering using Python and Scikit-Learn for efficient classification of spam and legitimate emails.

This repository contains Python code for text classification and analysis of e-commerce sales data. The script processes textual descriptions of products and categorizes them into predefined categories using a Naive Bayes classifier. It also includes various analysis and visualization methods to explore the dataset.

Scikit-learn friendly library to interpret, and prompt-engineer text datasets using large language models.

Time series forecasting with scikit-learn models

🏬 modelstore is a Python library that allows you to version, export, and save a machine learning model to your filesystem or a cloud storage provider.

Custom software development services specializing in AI/ML, data analysis, web scraping, and automation.

Machine Learning Containers for NVIDIA Jetson and JetPack-L4T

SAnDReS (Statistical Analysis of Docking Results and Scoring functions) is a free and open-source computational environment for the development of machine-learning models for the prediction of ligand-binding affinity. We developed SAnDReS using Python programming language, and SciPy, NumPy, scikit-learn, and Matplotlib libraries as a computational

Samples for Intel® oneAPI Toolkits

A toolkit for machine learning from time series

Flower: A Friendly Federated Learning Framework