Materials and IPython notebooks for "Python for Data Analysis" by Wes McKinney, published by O'Reilly Media
created at June 30, 2012, 6:39 p.m.
aka "Bayesian Methods for Hackers": An introduction to Bayesian methods + probabilistic programming with a computation/understanding-first, mathematics-second point of view. All in pure Python ;)
created at Jan. 14, 2013, 3:46 p.m.
⛔️ DEPRECATED – See https://github.com/ageron/handson-ml3 instead.
created at Feb. 16, 2016, 7:48 p.m.
🤖 Python examples of popular machine learning algorithms with interactive Jupyter demos and math being explained
created at Nov. 1, 2018, 4:34 a.m.
H2O is an Open Source, Distributed, Fast & Scalable Machine Learning Platform: Deep Learning, Gradient Boosting (GBM) & XGBoost, Random Forest, Generalized Linear Modeling (GLM with Elastic Net), K-Means, PCA, Generalized Additive Models (GAM), RuleFit, Support Vector Machine (SVM), Stacked Ensembles, Automatic Machine Learning (AutoML), etc.
created at March 3, 2014, 4:08 p.m.
A collection of tutorials and examples for solving and understanding machine learning and pattern classification tasks
created at March 30, 2014, 5:34 a.m.
Notebooks and code for the book "Introduction to Machine Learning with Python"
created at May 29, 2016, 6:29 p.m.
Jupyter notebooks from the scikit-learn video series
created at April 6, 2015, 2:08 a.m.
Recipes for using Python's pandas library
created at Dec. 21, 2013, 5:14 p.m.
Efficient Image Captioning code in Torch, runs on GPU
created at Nov. 20, 2015, 1:27 a.m.
Debugging, monitoring and visualization for Python Machine Learning and Data Science
created at May 15, 2019, 8:29 a.m.
Implementation of hyperparameter optimization/tuning methods for machine learning & deep learning models (easy&clear)
created at July 23, 2020, 3:30 a.m.
A scikit-learn compatible neural network library that wraps PyTorch
created at July 18, 2017, 12:13 a.m.
🛠 All-in-one web-based IDE specialized for machine learning and data science.
created at May 27, 2019, 4:55 p.m.
Implementation/Tutorial of using Automated Machine Learning (AutoML) methods for static/batch and online/continual learning
created at Sept. 9, 2022, 3:01 a.m.