shaadclt

Breast-Cancer-Detection-NaiveBayesClassifer

This project involves detecting breast cancer using the Naive Bayes classifier in Jupyter Notebook. Breast cancer detection is a crucial task in healthcare, as it aids in the early diagnosis and treatment of the disease. Through this project, we aim to explore and understand how the Naive Bayes classifier can be used for breast cancer detection.

BusinessCard-DataExtraction-OCR-NER

This project aims to extract structured data from business cards using a combination of OpenCV, PyTesseract, and spaCy.

Customer-Segmentation-KMeansClustering

This project involves segmenting customers using k-means clustering in Jupyter Notebook. Customer segmentation is a powerful technique used in marketing and business analytics to divide customers into distinct groups based on their behaviors, preferences, or demographics.

Gemini-LangChain-MultiPDF-Chatbot

Gemini-Powered-MultiPDF-Chatbot is a Streamlit application that leverages Google Generative AI Gemini and LangChain for conversational question-answering based on PDF documents. This chatbot allows users to ask questions related to the content of uploaded PDF files.

Heart-Disease-Prediction-KNN

This project focuses on predicting heart disease using the K-Nearest Neighbors (KNN) classification algorithm implemented in a Jupyter Notebook. It aims to provide a tool that can assist in early detection and diagnosis of heart disease based on given input features.

Height-Prediction-PolynomialRegression

This project involves predicting height based on age using polynomial regression in Jupyter Notebook. Polynomial regression is a variation of linear regression that models the relationship between the independent variable (age) and the dependent variable (height) as an nth-degree polynomial.

Image-Chat-Gemini-Pro-Vision

This Streamlit application allows users to chat with an image using Google's Generative AI, Gemini Pro Vision. Users can upload an image and enter a prompt, and the application will generate a response based on the image and prompt.

LLM-powered-PDF-Chatbot

This is a Streamlit-based PDF Chatbot powered by OpenAI's Language Models. The chatbot allows users to upload PDF files, extract text content, and ask natural language questions about the PDF content

Multiple-Disease-Prediction-System

The Multiple Disease Prediction project aims to create a user-friendly web application that allows users to input relevant medical information and receive predictions for different diseases.

Retail-Store-Database-Assistant

This project is a Retail Store Database Assistant that uses LangChain and Google Palm Language Model to interact with a MySQL database. Users can ask questions about the database, and the assistant will generate MySQL queries to retrieve relevant information.

shaadclt/LLM-powered-PDF-Chatbot

This is a Streamlit-based PDF Chatbot powered by OpenAI's Language Models. The chatbot allows users to upload PDF files, extract text content, and ask natural language questions about the PDF content

shaadclt/Gemini-LangChain-MultiPDF-Chatbot

Gemini-Powered-MultiPDF-Chatbot is a Streamlit application that leverages Google Generative AI Gemini and LangChain for conversational question-answering based on PDF documents. This chatbot allows users to ask questions related to the content of uploaded PDF files.

shaadclt/Gemini-LangChain-Chatbot

Gemini LangChain Chatbot is a Streamlit web application that allows users to have conversations with a language model powered by Google's Generative Gemini AI API and LangChain. This chatbot is designed to provide engaging and creative responses to user inputs.

shaadclt/Personal-Website

This repository contains the source code and assets for Mohamed Shaad's personal website. The website showcases Mohamed Shaad's background, skills, and projects as a self-taught data scientist from India.

shaadclt/Image-Chat-Gemini-Pro-Vision

This Streamlit application allows users to chat with an image using Google's Generative AI, Gemini Pro Vision. Users can upload an image and enter a prompt, and the application will generate a response based on the image and prompt.

shaadclt/3D-Surface-Prediction-Tensorflow

This project demonstrates the creation of a 3D surface prediction model using TensorFlow. The model is trained on a randomly generated dataset and visualizes the predicted surface in a 3D space.

shaadclt/CodeAssistant-Ollama-CodeLlama

This project provides a user-friendly Gradio interface that enables you to interact with the custom model based on CodeLlama model from Ollama, an open-source large language model platform. Simply enter your prompt in the textbox, and custom model will generate code based on your input.

shaadclt/CodeGeneration-Google-CodeGemma-2B

This repository demonstrates the potential of using Google's CodeGemma-2B Large Language Model (LLM) to assist in generating code.

shaadclt/Data-Science-Gen-AI-Playlist-2024

shaadclt/ElasticNet-MLflow-Regression

This repository contains code for training an ElasticNet regression model using MLflow. The model predicts the quality of wine based on various features.

shaadclt/Fine-Tune-Llama2-LoRA-QLoRA

This repository contains the code for fine-tuning the Llama-2-7b-chat model on a text instruction dataset using LoRA (Layer-wise Only Relevant Attention) and QLoRA (Quantized LoRA).

shaadclt/Handwritten-Digit-Recognition-Tensorflow

This project focuses on building a neural network using TensorFlow to recognize handwritten digits from the MNIST dataset. The model is a simple feedforward neural network with one hidden layer.

shaadclt/Image-to-Text-Generation

This repository contains a Jupyter Notebook that demonstrates how to use a pre-trained Vision Encoder-Decoder model for image captioning.

shaadclt/Invoice-Extractor-Gemini

The Invoice Extractor project aims to simplify the extraction of vital information from images, specifically focusing on invoices. By harnessing the capabilities of Gemini 1.5, Google's Multimodal Large Language Model (LLM), this application provides a robust solution for parsing invoice data with accuracy and efficiency.

shaadclt/Job-Fit-Analyzer-Gemini

Job Fit Analyzer is an application designed to assist in evaluating resumes based on given job descriptions. It utilizes machine learning and natural language processing techniques to analyze resumes and provide feedback on their suitability for a particular job role.

shaadclt/MediaPipePoseEstimation

shaadclt/MultilingualText-to-Image-Generation

This project demonstrates the generation of images based on textual prompts using a stable diffusion model. The text prompts are translated into multiple languages using Google Translate before generating images.

shaadclt/No-Code-ML-Model-Training

This project aims to provide a user-friendly interface for training machine learning models without the need for coding. It allows users to select a dataset, preprocess it, choose a model, train the model, and evaluate its performance, all through an intuitive web interface built with Streamlit.

shaadclt/Sentiment-Analysis-API

This API allows you to analyze the sentiment of text input. It uses TextBlob for sentiment analysis.

shaadclt/shaadclt

Special Repository

shaadclt/StableDiffusionApp

shaadclt/Text-to-Image-Generation

This project demonstrates how to generate images using diffusion models, specifically utilizing the Stable Diffusion model from Hugging Face's Transformers library.

shaadclt/TextAutocomplete-distilgpt2

This repository contains the code and resources for implementing text autocompletion using the DistilGPT-2 model from Hugging Face within a Jupyter Notebook environment.

shaadclt/TextAutocomplete-HuggingFace

This repository demonstrates how to use the HuggingFace Transformers library to implement text autocompletion in a Jupyter Notebook environment.

shaadclt/TextAutocomplete-LSTM-pytorch

This repository contains a Jupyter Notebook demonstrating text autocompletion using Long Short-Term Memory (LSTM) networks implemented in PyTorch.

shaadclt/TextAutocomplete-LSTM-Tensorflow

Text Autocomplete with TensorFlow LSTM is a project that demonstrates how to build a simple text autocomplete system using TensorFlow and LSTM (Long Short-Term Memory) networks. This project utilizes a dataset of frequently asked questions (FAQs) to train the LSTM model to predict the next word given a sequence of words.

shaadclt/TextGeneration-Llama3-HuggingFace

This repository demonstrates how to leverage the Llama3 large language model from Meta for text generation tasks using Hugging Face Transformers in a Jupyter Notebook environment.

shaadclt/Training-SmallLanguageModel-DiseaseSymptoms

This project utilizes the power of language models, specifically the GPT-2 model, to predict medical symptoms based on input text. By fine-tuning the GPT-2 model on a dataset containing disease names and associated symptoms, we train a language model to generate probable symptoms for a given disease.

shaadclt/Youtube-Transcription-Whisper

This jupyter notebook project empowers you to seamlessly download YouTube videos, extract their audio tracks, and transcribe the speech content using OpenAI's powerful Whisper model.

shaadclt/Youtube-Video-Summarizer

This is a Streamlit web application that summarizes YouTube videos using Youtube Transcript API and Google's Generative AI model Gemini Pro. The YouTube Video Summarizer allows users to input a YouTube video URL and generates a summary of the video content based on its transcript.