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Created a continuous, homogeneous, and structured 10 GB dataset from self obtained collections of unstructured intraday financial data. Generated features from indicators, statistics, and recent factors. Used multi-disciplined analysis to find feature importance. Attached labels of trends and sto... Read more

A comprehensive collection of Jupyter Notebook templates for data science tasks, developed to improve workflow efficiency and cover a wide range of topics, including exploratory data analysis, hypothesis testing, regression models, and machine learning models.

A collection of common functionality to simplify the design, training and evaluation of machine learning models based on pytorch with an emphasis on speech processing.

A delightful collection of free resources and tutorials for Python, data science, and machine learning. Dive into the world of coding, uncover the secrets of data analysis, and unleash the power of machine learning with our beginner-friendly materials. Join our community of learners and let's spo... Read more

A comprehensive, categorized collection of hundreds of research papers and surveys in Machine Learning and Natural Language Processing. This repository organizes papers by topics with direct links for easy access. Continuously updated to help researchers, students, and practitioners quickly find ... Read more

This repository contains a curated collection of 500+ Machine Learing Projects with code, detailing practical applications and insights into machine learning (ML) implimentation. The contents are organized to help you easily find relevant projects based on topics or specific ML algorithms.

Using machine learning on your anki collection to enhance the scheduling via semantic clustering and semantic similarity

A collection of tools for chemometrics and machine learning written in Julia.

A curated collection of simple, ready-to-use datasets for machine learning, data analysis, and tutorials.

A collection of MLOps interview questions and answers for data scientists and engineers. Learn how to apply MLOps principles and practices to automate and streamline machine learning workflows.