Free hands-on course with the implementation (in Python) and description of several computational, mathematical and statistical algorithms.

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

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

Machine learning in Python with scikit-learn MOOC

The Python code to reproduce the illustrations from The Hundred-Page Machine Learning Book.

Notebooks for the "A walk with fastai2" Study Group and Lecture Series

Implementation of Reinforcement Learning Algorithms. Python, OpenAI Gym, Tensorflow. Exercises and Solutions to accompany Sutton's Book and David Silver's course.

Kalman Filter book using Jupyter Notebook. Focuses on building intuition and experience, not formal proofs. Includes Kalman filters,extended Kalman filters, unscented Kalman filters, particle filte…

Notebooks for learning deep learning

Grokking Deep Reinforcement Learning

Starter app for fastai v3 model deployment on Render

this repository accompanies the book "Grokking Deep Learning"

Code repository for Think Bayes.

Text and supporting code for Think Stats, 2nd Edition

A collection of infrastructure and tools for research in neural network interpretability.

Advanced Deep Learning with Keras, published by Packt

Deep Learning with TensorFlow 2 and Keras, published by Packt

Open Machine Learning Course

NYU PSYCH-GA 3405.002 / DS-GS 3001.006 : Computational cognitive modeling

Code for Hands-on Unsupervised Learning Using Python (O'Reilly Media)

The fastai book, published as Jupyter Notebooks

A Code-First Introduction to NLP course

The fastai deep learning library

The 3rd edition of course.fast.ai

Lab Materials for MIT 6.S191: Introduction to Deep Learning

Practical notebooks for Khipu 2019, held in Universidad de la República in Montevideo.

COMS W4995 Applied Machine Learning - Spring 20

Intermediate Machine Learning with Scikit-learn, 4h interactive workshop

Advanced Machine Learning with Scikit-learn part I