Developed a predictive model for retail customer churn using Microsoft Azure Machine Learning Studio

Leveraging advanced data science methodologies. Key tasks included extensive data preprocessing (data cleansing, normalization, feature engineering) and implementing a Boosted Decision Tree algorithm for high-accuracy predictions

Utilized Python libraries (NumPy, Pandas, Scikit-learn, azureml-sdk) and integrated PowerBI for detailed visualizations and interactive dashboards. Employed cloud-based solutions for scalable model deployment, yielding actionable insights for customer retention strategies and optimizing business performance

Tensorlab simplifies time series data modeling using Keras and TensorFlow. It automates data preprocessing, model management, and training progress monitoring

Ideal for deep learning workflows, it supports CNNs, RNNs, and GANs, and integrates with Python libraries like NumPy and Pandas for efficient and high-precision results

Squirrel supports datasets ranging from tiny to medium, providing tools for data acquisition, cleansing, and visualization. It aims to empower software developers to derive insights from data, bridging the gap between business users and complex data analytics. The framework is designed for ease of use, with APIs for various data operations, and is actively developed to incorporate user feedback and new features

It's a modular and highly configurable framework designed to streamline machine learning experiments. It supports various run environments, including processes, Docker containers, and Kubernetes pods, and integrates with multiple logging backends like MLflow, TensorBoard, and Trains

The framework simplifies experiment setup, execution, and monitoring, making it ideal for individual data scientists, researchers, and small teams. With features like systematic logging, parameterized experiments, and seamless integration with existing ML projects, ExperimentEase enhances productivity and efficiency in resource-constrained environments

It covers the strategic use of finetuning, data preparation, instruction tuning, and training and evaluation processes. This course equips you to enhance model performance, ensuring efficient and effective use of fine-tuned language models for superior NLP project outcomes

Provides a comprehensive collection of models and algorithms for Markov Chain Monte Carlo (MCMC) simulations

It includes implementations of the DWPSDE model, Ricker model, and adaptive updating algorithms, among others

This repository is designed to facilitate advanced research and practical applications in stochastic processes, offering utilities and tools for efficient MCMC simulations and analysis

It's a powerful automation server designed to streamline code deployment through continuous integration and delivery (CI/CD)

It supports various technologies, including Python, HTML, CSS, and Docker, to ensure seamless code flow and efficient deployment processes. The repository includes essential files like Dockerfile, Jenkinsfile, and deployment configurations, making it a comprehensive solution for automating and managing software development pipelines

In-Context Rag for contextual response refinement, and LLAMA2 for generating human-like responses. This system leverages function calling to interact with various components, ensuring accurate and contextually appropriate answers to user queries. It combines advanced data science techniques with large language models to enhance the efficiency and relevance of automated responses