Displaying 1 to 20 from 87 results

tpot - A Python Automated Machine Learning tool that optimizes machine learning pipelines using genetic programming

  •    Python

Consider TPOT your Data Science Assistant. TPOT is a Python Automated Machine Learning tool that optimizes machine learning pipelines using genetic programming.TPOT will automate the most tedious part of machine learning by intelligently exploring thousands of possible pipelines to find the best one for your data.

nolearn - Combines the ease of use of scikit-learn with the power of Theano/Lasagne

  •    Python

nolearn contains a number of wrappers and abstractions around existing neural network libraries, most notably Lasagne, along with a few machine learning utility modules. All code is written to be compatible with scikit-learn. We recommend using venv (when using Python 3) or virtualenv (Python 2) to install nolearn.

skorch - A scikit-learn compatible neural network library that wraps pytorch

  •    Python

A scikit-learn compatible neural network library that wraps PyTorch. To see a more elaborate example, look here.

handson-ml - A series of Jupyter notebooks that walk you through the fundamentals of Machine Learning and Deep Learning in python using Scikit-Learn and TensorFlow

  •    Jupyter

First, you will need to install git, if you don't have it already. If you want to go through chapter 16 on Reinforcement Learning, you will need to install OpenAI gym and its dependencies for Atari simulations.

PythonDataScienceHandbook - Python Data Science Handbook: full text in Jupyter Notebooks

  •    Jupyter

This repository contains the entire Python Data Science Handbook, in the form of (free!) Jupyter notebooks. Run the code using the Jupyter notebooks available in this repository's notebooks directory.

dive-into-machine-learning - Dive into Machine Learning with Python Jupyter notebook and scikit-learn!


I learned Python by hacking first, and getting serious later. I wanted to do this with Machine Learning. If this is your style, join me in getting a bit ahead of yourself. I suggest you get your feet wet ASAP. You'll boost your confidence.

python-machine-learning-book - The "Python Machine Learning (1st edition)" book code repository and info resource

  •    Jupyter

This GitHub repository contains the code examples of the 1st Edition of Python Machine Learning book. If you are looking for the code examples of the 2nd Edition, please refer to this repository instead. What you can expect are 400 pages rich in useful material just about everything you need to know to get started with machine learning ... from theory to the actual code that you can directly put into action! This is not yet just another "this is how scikit-learn works" book. I aim to explain all the underlying concepts, tell you everything you need to know in terms of best practices and caveats, and we will put those concepts into action mainly using NumPy, scikit-learn, and Theano.

python-machine-learning-book-2nd-edition - The "Python Machine Learning (2nd edition)" book code repository and info resource

  •    Jupyter

Python Machine Learning, 2nd Ed. To access the code materials for a given chapter, simply click on the open dir links next to the chapter headlines to navigate to the chapter subdirectories located in the code/ subdirectory. You can also click on the ipynb links below to open and view the Jupyter notebook of each chapter directly on GitHub.

scikit-learn-videos - Jupyter notebooks from the scikit-learn video series

  •    Jupyter

This video series will teach you how to solve machine learning problems using Python's popular scikit-learn library. It was featured on Kaggle's blog in 2015. There are 9 video tutorials totaling 4 hours, each with a corresponding Jupyter notebook. The notebook contains everything you see in the video: code, output, images, and comments.

mlcourse_open - OpenDataScience Machine Learning course. Both in English and Russian

  •    Python

This is the list of published articles on medium.com 🇬🇧, habr.com 🇷🇺, and jqr.com 🇨🇳. Icons are clickable. Also, links to Kaggle Kernels (in English) are given. This way one can reproduce everything without installing a single package. Assignments will be announced each week. Meanwhile, you can pratice with demo versions. Solutions will be discussed in the upcoming run of the course.

featuretools - automated feature engineering

  •    Python

Featuretools is a python library for automated feature engineering. See the documentation for more information. Below is an example of using Deep Feature Synthesis (DFS) to perform automated feature engineering. In this example, we apply DFS to a multi-table dataset consisting of timestamped customer transactions.

scikit-plot - An intuitive library to add plotting functionality to scikit-learn objects.

  •    Python

Scikit-plot is the result of an unartistic data scientist's dreadful realization that visualization is one of the most crucial components in the data science process, not just a mere afterthought. Gaining insights is simply a lot easier when you're looking at a colored heatmap of a confusion matrix complete with class labels rather than a single-line dump of numbers enclosed in brackets. Besides, if you ever need to present your results to someone (virtually any time anybody hires you to do data science), you show them visualizations, not a bunch of numbers in Excel.

xcessiv - A web-based application for quick, scalable, and automated hyperparameter tuning and stacked ensembling in Python

  •    Python

Stacked ensembles are simple in theory. You combine the predictions of smaller models and feed those into another model. However, in practice, implementing them can be a major headache. Xcessiv holds your hand through all the implementation details of creating and optimizing stacked ensembles so you're free to fully define only the things you care about.

interpret - Fit interpretable models. Explain blackbox machine learning.

  •    C++

Historically, the most intelligible models were not very accurate, and the most accurate models were not intelligible. Microsoft Research has developed an algorithm called the Explainable Boosting Machine (EBM)* which has both high accuracy and intelligibility. EBM uses modern machine learning techniques like bagging and boosting to breathe new life into traditional GAMs (Generalized Additive Models). This makes them as accurate as random forests and gradient boosted trees, and also enhances their intelligibility and editability. In addition to EBM, InterpretML also supports methods like LIME, SHAP, linear models, partial dependence, decision trees and rule lists. The package makes it easy to compare and contrast models to find the best one for your needs.

scikit-learn-doc-cn - scikit-learn机器学习库中文文档翻译项目

  •    HTML


python-cheat-sheet - Python Cheat Sheet NumPy, Matplotlib

  •    Jupyter

This rep is a growing list of Python cheat sheets, tailored for Data Science. If you want to install a package individually, go into the corresponding <package-name>.md file for instructions on how to install.