Starred repositories
Pocket Flow: Codebase to Tutorial
FULL Augment Code, Claude Code, Cluely, CodeBuddy, Comet, Cursor, Devin AI, Junie, Kiro, Leap.new, Lovable, Manus, NotionAI, Orchids.app, Perplexity, Poke, Qoder, Replit, Same.dev, Trae, Traycer AI…
Use OpenAI GPTs for Free: https://gptcall.net/
Repository of instructions for Programming-specific GPT models
🏅 Collection of Kaggle Solutions and Ideas 🏅
Curated list of data science interview questions and answers
Learn how to design large-scale systems. Prep for the system design interview. Includes Anki flashcards.
Mixture of Decision Trees for Interpretable Machine Learning
Code for the Data Engineering Zoomcamp
Automated Machine Learning with scikit-learn
zoofs is a python library for performing feature selection using a variety of nature-inspired wrapper algorithms. The algorithms range from swarm-intelligence to physics-based to Evolutionary. It's…
Data search & enrichment library for Machine Learning → Easily find and add relevant features to your ML & AI pipeline from hundreds of public and premium external data sources, including open & co…
This repository offers a goldmine of materials for students of computer vision, natural language processing, and machine learning operations.
Free MLOps course from DataTalks.Club
Repository to track the progress in Natural Language Processing (NLP), including the datasets and the current state-of-the-art for the most common NLP tasks.
A Python Library for Outlier and Anomaly Detection, Integrating Classical and Deep Learning Techniques
Collection of papers and resources for data augmentation for NLP.
Scale complete ML development with Amazon SageMaker Studio
mRMR (minimum-Redundancy-Maximum-Relevance) for automatic feature selection at scale.
Minimal examples of data structures and algorithms in Python
Matplotlib style sheets to nicely format figures for scientific papers, thesis and presentations while keeping them fully editable in Adobe Illustrator.
A complete computer science study plan to become a software engineer.
2-2000x faster ML algos, 50% less memory usage, works on all hardware - new and old.
Machine Learning Interviews from FAANG, Snapchat, LinkedIn. I have offers from Snapchat, Coupang, Stitchfix etc. Blog: mlengineer.io.