smazzanti

Data-Science-Interview-Questions-Answers. Curated list of data science interview questions and answers

Algorithms-for-Optimization-Python. Unofficial implementation in Python porting of the book "Algorithms for Optimization" (2019) MIT Press by By Mykel J. Kochenderfer and Tim A. Wheeler

school-of-sre. At LinkedIn, we are using this curriculum for onboarding our entry-level talents into the SRE role.

mrmr. mRMR (minimum-Redundancy-Maximum-Relevance) for automatic feature selection at scale.

experiments-performance_estimation. Comparing different performance estimation methods for time series forecasting tasks

tsdl. The Time Series Data Library (TSDL) was created by Rob Hyndman, Professor of Statistics at Monash University, Australia.

VennABERS. Fast implementation of Venn-ABERS probabilistic predictors

data-engineering-zoomcamp. Data Engineering Zoomcamp is a free 9-week course on building production-ready data pipelines. The next cohort starts in January 2026. Join the course here 👇🏼

MLOps. MLOps examples

dowhy. DoWhy is a Python library for causal inference that supports explicit modeling and testing of causal assumptions. DoWhy is based on a unified language for causal inference, combining causal graphical models and potential outcomes frameworks.

awesome-public-datasets. A topic-centric list of HQ open datasets.

mlops-zoomcamp. Free MLOps course from DataTalks.Club

nannyml. nannyml: post-deployment data science in python

ml_ops. Jupyter Notebook

super-gradients. Easily train or fine-tune SOTA computer vision models with one open source training library. The home of Yolo-NAS.

lifetime_value. Jupyter Notebook

MAPIE. A scikit-learn-compatible library for estimating prediction intervals and controlling risks, based on conformal predictions.

nmt. TensorFlow Neural Machine Translation Tutorial

dedupe. :id: A python library for accurate and scalable fuzzy matching, record deduplication and entity-resolution.

applied-ml. 📚 Papers & tech blogs by companies sharing their work on data science & machine learning in production.

awesome-mlops. A curated list of references for MLOps

data-science-on-aws. AI and Machine Learning with Kubeflow, Amazon EKS, and SageMaker

cuttle-cli. Cuttle automates the transformation of your Python notebook into deployment-ready projects (API, ML pipeline, or just a Python script)

cracking-the-data-science-interview. A Collection of Cheatsheets, Books, Questions, and Portfolio For DS/ML Interview Prep

keras-wtte-rnn. Demo Weibull Time-to-event Recurrent Neural Network in Keras

shapash. 🔅 Shapash: User-friendly Explainability and Interpretability to Develop Reliable and Transparent Machine Learning Models

airflow-training. Airflow training for the crunch conf

wtte-rnn. WTTE-RNN a framework for churn and time to event prediction

Real-time-stock-market-prediction. In this repository, I have developed the entire server-side principal architecture for real-time stock market prediction with Machine Learning. I have used Tensorflow.js for constructing ml model architecture, and Kafka for real-time data streaming and pipelining.

KerasQuantileModel. Quantile Regression using Deep Learning. An alternative to Bayesian models to get uncertainty.

transformers. 🤗 Transformers: the model-definition framework for state-of-the-art machine learning models in text, vision, audio, and multimodal models, for both inference and training.

ai-platform-samples. Official Repo for Google Cloud AI Platform. Find samples for Vertex AI, Google Cloud's new unified ML platform at: https://github.com/GoogleCloudPlatform/vertex-ai-samples

MEDIUM_NoteBook. Repository containing notebooks of my posts on Medium

spectra-review-paper-competition. Competition for best expository article on cutting-edge ML research

acme. A library of reinforcement learning components and agents

ppscore. Predictive Power Score (PPS) in Python

KDD20-tutorial. Tutorial on Automated Machine Learning at KDD 2020

netron. Visualizer for neural network, deep learning and machine learning models

gcn. Implementation of Graph Convolutional Networks in TensorFlow

Supervised-Learning-on-Relational-Databases-with-GNNs. Code to reproduce the results in the paper Supervised Learning on Relational Databases with Graph Neural Networks.

DomainBed. DomainBed is a suite to test domain generalization algorithms

awesome-causality-algorithms. An index of algorithms for learning causality with data

data-science-interviews. Data science interview questions and answers