An open-source data logging library for machine learning models and data pipelines. 📚 Provides visibility into data quality & model performance over time. 🛡️ Supports privacy-preserving data collection, ensuring safety & robustness. 📈

Free Open-source ML observability course for data scientists and ML engineers. Learn how to monitor and debug your ML models in production.

A curated list of awesome open source tools and commercial products for monitoring data quality, monitoring model performance, and profiling data 🚀

This repository comprises of the projects and assignments that i have completed during my tenure at Great Lakes for the course program PGP-AIML. This repository also includes the lab work thatwas done during the classes and even those that were given as assessments.

Python Open-source package that ensures robust and reliable ML models deployments

In this data science course, you will be given clear explanations of machine learning theory combined with practical scenarios and hands-on experience building, validating, and deploying machine learning models. You will learn how to build and derive insights from these models using Python, and Azure Notebooks.

Built a Deep learning Model for Sequential Sentence Classification, for Converting “Harder to Read” text into “Easier to Read ” text.

Projects on Machine Learning Model using Python

predict the rent price of a house based on its surface

Neural Networks and Deep Learning Models

Improve the speed and accuracy of detecting and localizing brain tumors based on MRI scans.

Analyze used devices dataset, build a model to develop a dynamic pricing strategy for used/refurbished devices, identify factors that significantly influence price.

Multiple Linear Regression and Logistic Regression using the Boston housing dataset

This repository contains an academic project developed in jupyter notebook using python language and machine learning algorithms.

Yellowbrick is an useful machine learning visualization library for visualizing model performance. This Jupyter notebook gives an example for using yellowbrick to visualize model performance of a ternary classification task.