The repository provides code for running inference with the SegmentAnything Model (SAM), links for downloading the trained model checkpoints, and example notebooks that show how to use the model.

Python Data Science Handbook: full text in Jupyter Notebooks

Learn how to design, develop, deploy and iterate on production-grade ML applications.

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 👇🏼

CLIP (Contrastive Language-Image Pretraining), Predict the most relevant text snippet given an image

A game theoretic approach to explain the output of any machine learning model.

Python programs, usually short, of considerable difficulty, to perfect particular skills.

A guidance language for controlling large language models.

Neural Networks: Zero to Hero

Companion webpage to the book "Mathematics For Machine Learning"

An open-source, low-code machine learning library in Python

Python code for "Probabilistic Machine learning" book by Kevin Murphy

A scikit-learn compatible neural network library that wraps PyTorch

Machine Learning Foundations: Linear Algebra, Calculus, Statistics & Computer Science

https://www.youtube.com/channel/UC34rW-HtPJulxr5wp2Xa04w?sub_confirmation=1

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

Algorithms for outlier, adversarial and drift detection

2-2000x faster ML algos, 50% less memory usage, works on all hardware - new and old.

Code, Notebooks and Examples from Practical Business Python

Interpretable ML package 🔍 for concise, transparent, and accurate predictive modeling (sklearn-compatible).

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

Python code for part 2 of the book Causal Inference: What If, by Miguel Hernán and James Robins

PiML (Python Interpretable Machine Learning) toolbox for model development & diagnostics

Self-study on Larry Wasserman's "All of Statistics"

A collection of code snippets from the publication Daily Dose of Data Science on Substack: http://www.dailydoseofds.com/

Material used for Deep Learning related workshops for Machine Learning Tokyo (MLT)

Jupyter notebooks for learning how to use SimpleITK

NMA deep learning course

The open source repository for the Causal Modeling in Machine Learning Workshop at Altdeep.ai @ www.altdeep.ai/courses/causalML