Realtime Sign Language Detection: Deep learning model for accurate, real-time recognition of sign language gestures using Python and TensorFlow.

End-to-end ML pipeline built on a 45K-record ITSM dataset to automate incident triage, predict high-priority tickets (96% Acc.), and forecast incident volume for proactive resource planning.

To import data from multiple sources, clean and wrangle data, perform exploratory data analysis (EDA), and create meaningful data visualizations. I will then predict future trends from data by developing linear, multiple, polynomial regression models & pipelines and learn how to evaluate them.

Master data analysis, visualization, Python, machine learning, and real-world projects to drive data-driven decisions and advance your career in tech, business, or analytics.

Model Evaluation is the process through which we quantify the quality of a system’s predictions. To do this, we measure the newly trained model performance on a new and independent dataset. This model will compare labeled data with it’s own predictions.

Label-Free Model Evaluation and Weighted Uncertainty Sample Selection for Domain Adaptive Instance Segmentation

SeqFlipAttention is a forward‑looking PyTorch demonstration of sequence‑to‑sequence learning enhanced by attention, trained on a synthetic reverse‑sequence task and complete with training scripts, loss and accuracy visualizations, and a quantitative analysis of attention’s impact on performance.

machine learning techniques to predict company defaults by optimizing the trade-off between recall (minimizing false negatives) and precision (avoiding false positives). Logistic Regression and Random Forest models were trained, with emphasis on recall to ensure accurate identification of high-risk companies.

This project implements Google Cloud's Vertex AI to develop a machine learning model that predicts loan repayment risks using a tabular dataset. It encompasses data preparation, model training, evaluation, deployment, and prediction processes.

This repository contains code for evaluating different machine learning models for classifying fake news. The dataset used for this evaluation consists of labeled news articles as either "REAL" or "FAKE". Three popular classifiers, Support Vector Machine (SVM), Decision Tree, and Logistic Regression, are trained and evaluated on this dataset.

This repo contains a comprehensive tutorial on machine learning with practical implementations and examples using Python.

I developed a sophisticated ML model using LLMs to predict user preferences in chatbot interactions.implemented a comprehensive data preprocessing pipeline,including feature extraction and encoding,to optimize performance. conducted extensive hyperparameter tuning and evaluation, enhancing accuracy and in AI-driven conversational systems.

IBM Generative AI Engineering Professional Certificate

Data Preprocessing, Data Cleaning, Fine-tuning the Hyperparameters,

An advanced machine learning project deploying a model for Titanic passenger survival prediction, including deployment on ngrok for easy access.

This repository contains mini projects inData science in python with notebook files

BC4AI：Blockchain Used to Guarantee Credibility of AI Model Evaluations;利用区块链来保证算法模型的真实性

Building a model to predict demand of shared bikes. It will be used by the management to understand how exactly the demands vary with different features. They can accordingly manipulate the business strategy to meet the demand levels.

This repository contains a machine learning project that classifies SONAR reading data to distinguish between rocks and mines. It implements various classification models,evaluates their performance,and features a user-friendly web application deployed with Streamlit for real-time predictions. The project is aimed to help in safe marine operations.