NoCodeAIML

No Code AI/ML platform - Community Edition

Video credits: Uday Kiran

Typical No Code AI/ML Platform will have features like drag and drop, data source connectivity, social media analysis, analytics, and content analysis.No code AI/ML platform can be built using python and streamlit, which helps in images analysis. The analytical data in the no-code AI/ML platform is imported, validated, cleansed, trained, and testing of the model. New scenarios come in due to unseen data and the model is refined for predictive analytics.

Planning to add more features, please contribute.

check out my article on AI4 blog No Code AI/ML Platform

Use Cases

1.analyze text messages
2.classify shipping documents
3.categorize social media pictures
4.categorize Instagram pictures
5.check onboarding documents
6.categorize startups by description
7.filter startup databases for investment decisions
8.categorizing emails
9.tracking keyword engagement
10.social listening
11.categorizing app crash reports using NLP
12.moderate user-generated content
13.insurance claims identification
14.quality inspection
15.auto-tagging real estate images,
16.tagging email attachments
17.categorizing support tickets
18.analyze text responses of surveys
19.track social sentiments
20.categorizing SMS responses
21.recognize shipping documents

Features

1.Data Visualization
2.Data Analysis
3.Machine Learning Algorithms
4.Image Analysis
5.NLP & NLU
6.WebRTC API
7.Drawing API
8.Grids for tables

Instructions for setting up locally - webapp

Ensure that python3 and streamlit

2.git clone this repository

git clone https://github.com/bhagvank/NoCodeAIML.git

Install the required packages

pip3 install -r requirements.txt

Run streamlit

streamlit run main.py

No Code AI/ML platform

Related tags

Overview

NoCodeAIML

Use Cases

Features

Instructions for setting up locally - webapp

Owner

Bhagvan Kommadi

Res2Net for Instance segmentation and Object detection using MaskRCNN

Forecasting directional movements of stock prices for intraday trading using LSTM and random forest

[CVPR2021] DoDNet: Learning to segment multi-organ and tumors from multiple partially labeled datasets

level1-image-classification-level1-recsys-09 created by GitHub Classroom

Recommendation algorithms for large graphs

Some experiments with tennis player aging curves using Hilbert space GPs in PyMC. Only experimental for now.

The repo for the paper "I3CL: Intra- and Inter-Instance Collaborative Learning for Arbitrary-shaped Scene Text Detection".

Few-shot Neural Architecture Search

In this project we combine techniques from neural voice cloning and musical instrument synthesis to achieve good results from as little as 16 seconds of target data.

The official implementation of VAENAR-TTS, a VAE based non-autoregressive TTS model.

Generalized Data Weighting via Class-level Gradient Manipulation

[CVPR'21] Projecting Your View Attentively: Monocular Road Scene Layout Estimation via Cross-view Transformation

The official code repo of "HTS-AT: A Hierarchical Token-Semantic Audio Transformer for Sound Classification and Detection"

Mapping Conditional Distributions for Domain Adaptation Under Generalized Target Shift

DL & CV-based indicator toolset for the vehicle drivers via live dash-cam footage.

Many Class Activation Map methods implemented in Pytorch for CNNs and Vision Transformers. Including Grad-CAM, Grad-CAM++, Score-CAM, Ablation-CAM and XGrad-CAM

Multi Agent Reinforcement Learning for ROS in 2D Simulation Environments

A Python Library for Graph Outlier Detection (Anomaly Detection)

ML-Decoder: Scalable and Versatile Classification Head

Equivariant GNN for the prediction of atomic multipoles up to quadrupoles.