🛠 All-in-one web-based IDE specialized for machine learning and data science.

Machine Learning (Beginners Hub), information(courses, books, cheat sheets, live sessions) related to machine learning, data science and python is available

This repo contains a collection of code samples and utilities for integrating Google Cloud AI & ML into Google Workspace.

Reinforcement learning algorithm implementations and ML experimentation workspace

Setting up an MLflow Workspace with Docker

Code and tutorials to create an Azure Machine Learning workspace from the Azure Portal, CLI, SDK and ARM templates.

ML projects require a lot of data. Data is available on the internet but quality datasets are restricted for research purpose only. So, we aim to create a Nepali Chat database. We will basically make a chat group in any platform and pledge to chat for about 5 minutes a day for few months. Our small contribution could create a Nepali Database which could be used by researchers and data scientists in building a better Nepal. You will be invited to a slack workspace after you fill up this form.

AUTOMATA02 – An intelligent workspace automation hub built with Python. It combines smart file sorting, UI workflow learning, and autonomous data reporting into one system. Powered by pandas, Streamlit, and AI/ML for intelligent classification, anomaly detection, and personalized dashboards.

Monitor several Azure Machine Learning (AML) workspaces and create a dashboard to visualize what's happening

GitHub Action - Download a Terraform plan from workspace artifacts and apply with AzureRM backend (Used with: `Pwd9000-ML/terraform-azurerm-plan`)

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GitHub Action - Create a Terraform plan with AzureRM backend and uploads plan as workspace artifact (Used with: `Pwd9000-ML/terraform-azurerm-apply`)

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Workspace for dqm-ml-v2 packages

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🧠A hands-on workspace for practicing machine learning concepts, data preprocessing, and experimenting with small ML projects. This repo includes foundational Python scripts, real-world mini-projects, and experiments that reflect a progressive learning journey in applied machine learning.

Template for ML projects development and deployment using Databricks platform (on a unique workspace)

Basic and reusable components for ML experiments.

All-in-one web-based IDE specialized for machine learning and data science.

ML experiments and snippets

Some notes and examples for SysML Kernels

Documentation of Setting up Parallel & Distributed ML Workspace in your systems. And to work seamlessly without error. Package to easily setup a environment in the group of systems

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A repository for machine learning (ML) algorithms created from scratch using native python and fundamental packages such as numpy. This repository is my personal workspace for deepening the understanding of the inner workings of ML algorithms.

databricks-dab-lab is an end-to-end lab that shows how to deploy Databricks Asset Bundles (DABs) with GitHub Actions, using Terraform to provision an Azure Databricks workspace + cluster, then deploying and running three jobs in sequence (data setup → ETL → ML training).

Its an ML powered Q&A forum where a user can ask questions which will be answered by users specialized in that particular category. The tags to which the question belongs is automatically generated by the ML model to ensure that the question reaches the right audience. Users can also create communities for their college or workspace. Users who actively participate in discussions are given points according to their involvement, which can later help them in the job market. Technologies used: ReactJS, NodeJS, Flask, Tensorflow

ai and ml practice workspace

ML workspace

azure-ml-workspace

A collection of ML models I've trained—from classic algorithms to neural networks. Built as part of my interview prep and learning journey. Each model includes code, metrics, and notes. More models coming soon as I keep experimenting and improving.