Displaying 1 to 7 from 7 results

mlr - mlr: Machine Learning in R

  •    R

Please cite our JMLR paper [bibtex]. Some parts of the package were created as part of other publications. If you use these parts, please cite the relevant work appropriately. An overview of all mlr related publications can be found here.

ISLR-python - An Introduction to Statistical Learning (James, Witten, Hastie, Tibshirani, 2013): Python code

  •    Jupyter

This repository contains Python code for a selection of tables, figures and LAB sections from the book 'An Introduction to Statistical Learning with Applications in R' by James, Witten, Hastie, Tibshirani (2013). 2018-01-15: Minor updates to the repository due to changes/deprecations in several packages. The notebooks have been tested with these package versions. Thanks @lincolnfrias and @telescopeuser.

Skater - Python Library for Model Interpretation/Explanations

  •    Python

Skater is a unified framework to enable Model Interpretation for all forms of model to help one build an Interpretable machine learning system often needed for real world use-cases(** we are actively working towards to enabling faithful interpretability for all forms models). It is an open source python library designed to demystify the learned structures of a black box model both globally(inference on the basis of a complete data set) and locally(inference about an individual prediction). The project was started as a research idea to find ways to enable better interpretability(preferably human interpretability) to predictive "black boxes" both for researchers and practioners. The project is still in beta phase.

Coursera-Machine-Learning - Coursera Machine Learning - Python code

  •    Jupyter

This repository contains python implementations of certain exercises from the course by Andrew Ng. For a number of assignments in the course you are instructed to create complete, stand-alone Octave/MATLAB implementations of certain algorithms (Linear and Logistic Regression for example). The rest of the assignments depend on additional code provided by the course authors. For most of the code in this repository I have instead used existing Python implementations like Scikit-learn.

h2o.js - Node.js bindings to H2O, the open-source prediction engine for big data science.

  •    CoffeeScript

This Node.js / io.js module provides access to the H2O JVM (and extensions thereof), its objects, its machine-learning algorithms, and modeling support (basic munging and feature generation) capabilities. It is designed to bring H2O to a wider audience of data and machine learning devotees that work exclusively with Javascript, for building machine learning applications or doing data munging in a fast, scalable environment without any extra mental anguish about threads and parallelism.

data-science-live-book - An open source book to learn data science, data analysis and machine learning, suitable for all ages!

  •    TeX

This book is now available at Amazon in [Kindle]( Link: http://a.co/d/dIj1XwD) Black & White and color 📗 🚀. Most of the written R code can be used in real scenarios! I worked on the funModeling R package at the same time, so it is used many times in the book.