https://github.com/shiningflash/machine-learning
Explore practical machine learning projects, from predicting taxi fares to visualizing neural networks, making AI concepts simple and accessible for everyone!
https://github.com/shiningflash/machine-learning
data-science deep- machine-learning neural-network python visualization webapp
Last synced: 28 days ago
JSON representation
Explore practical machine learning projects, from predicting taxi fares to visualizing neural networks, making AI concepts simple and accessible for everyone!
- Host: GitHub
- URL: https://github.com/shiningflash/machine-learning
- Owner: shiningflash
- Created: 2020-07-23T05:16:16.000Z (almost 6 years ago)
- Default Branch: master
- Last Pushed: 2024-11-26T00:27:19.000Z (over 1 year ago)
- Last Synced: 2025-12-27T04:19:42.522Z (5 months ago)
- Topics: data-science, deep-, machine-learning, neural-network, python, visualization, webapp
- Language: Jupyter Notebook
- Homepage:
- Size: 1.85 MB
- Stars: 0
- Watchers: 0
- Forks: 1
- Open Issues: 1
-
Metadata Files:
- Readme: README.md
Awesome Lists containing this project
README
# Machine Learning Projects
Welcome to my collection of machine learning projects! These repositories showcase a range of machine learning and deep learning techniques applied to solve real-world problems, visualize data, and create robust models. Each project is detailed below with its purpose and relevant technologies.
## Projects Overview
### [Neural Network Visualizer and Digit Prediction](https://github.com/shiningflash/Neural-Network-Visualizer-And-Digit-Prediction)
A dynamic visualizer for understanding how neural networks operate, with an implementation to predict handwritten digits using a trained model. The project combines visualization techniques with interactive tools to demystify neural network layers.
### [NASA Space Apps COVID-19 Challenge](https://github.com/shiningflash/NASA-Space-Apps-Covid-19-Challenge-2020)
Developed for NASA's Space Apps Hackathon 2020, this project explores innovative solutions to COVID-19-related challenges, leveraging machine learning models and data analytics for meaningful insights.
### [Sentiment Analysis using Deep Learning](https://github.com/BONDHU-BOT/Sentiment-Analysis-using-Deep-Learning)
A deep learning-based approach to analyze sentiment in text data, classifying emotions as positive, negative, or neutral. Techniques include LSTM and GRU models for improved sentiment prediction accuracy.
### [Emotion Detection using Deep Learning](https://github.com/BONDHU-BOT/Emotion-Detection-using-Deep-Learning)
This project focuses on detecting emotions such as happiness, anger, and sadness in text using advanced deep learning architectures. Practical applications include chatbots and customer feedback analysis.
### [Intent Classification using Deep Learning](https://github.com/BONDHU-BOT/Intent-Classification-using-Deep-Learning)
A robust deep learning pipeline for classifying user intent from text inputs. This project is ideal for enhancing conversational AI systems by improving intent detection and response accuracy.
### [Named Entity Recognition using Deep Learning](https://github.com/BONDHU-BOT/Named-Entity-Recognition-using-Deep-Learning)
An implementation of Named Entity Recognition (NER) models to extract entities like names, locations, and organizations from text. The project demonstrates how deep learning can streamline NER tasks for NLP applications.
### [Machine Learning Web App using Streamlit](https://github.com/shiningflash/Machine-Learning/tree/master/ML_WebApp)
A user-friendly web application built with Streamlit to demonstrate various machine learning models in action. It provides an intuitive interface for model selection, predictions, and performance visualization.
### [New York City Taxi Fare Prediction](https://github.com/shiningflash/New-York-City-Taxi-Fare-Prediction)
A regression-based model predicting taxi fares in New York City using datasets with geospatial and temporal features. This project showcases data preprocessing, feature engineering, and model optimization techniques.
### [Neural Networks and Deep Learning - Coursera Assignment](https://github.com/shiningflash/Machine-Learning/tree/master/Neural%20Networks%20and%20Deep%20Learning%20-%20Coursera/Assignments)
Coursework assignments from the "Neural Networks and Deep Learning" specialization on Coursera. These assignments provide hands-on implementation of neural network concepts, including forward and backward propagation.
---
Feel free to explore the repositories and gain insights into the methodologies and technologies used. Contributions, feedback, and discussions are always welcome!