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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.

machine_learning_basics - Plain python implementations of basic machine learning algorithms

  •    Jupyter

This repository contains implementations of basic machine learning algorithms in plain Python (Python Version 3.6+). All algorithms are implemented from scratch without using additional machine learning libraries. The intention of these notebooks is to provide a basic understanding of the algorithms and their underlying structure, not to provide the most efficient implementations. After several requests I started preparing notebooks on how to preprocess datasets for machine learning. Within the next months I will add one notebook for each kind of dataset (text, images, ...). As before, the intention of these notebooks is to provide a basic understanding of the preprocessing steps, not to provide the most efficient implementations.

python_intro - Jupyter notebooks in Russian

  •    Jupyter

В курсе рассматриваются основы програмирования на языке Python, а также есть материал про базовые алгоритмы и структуры данных. Более расширенная версия именно по основам Python – в этом репозитории курса ВШЭ "Интеллектуальный анализ данных". Курс разработан в виде тетрадок Jupyter - это удобное средство представления материала с интерактивным выполнением кода. Инструкции по локальному развертыванию сервера Jupyter для использования тетрадок представлены в тетрадке с обзором средств разработки.

pynamical - Pynamical is a Python package for modeling and visualizing discrete nonlinear dynamical systems, chaos, and fractals

  •    Python

pynamical uses pandas, numpy, and numba for fast simulation, and matplotlib for visualizations and animations to explore system behavior. Compatible with Python 2 and 3. Pynamical comes packaged with the logistic map, the Singer map, and the cubic map predefined. The models may be run with a range of parameter values over a set of time steps, and the resulting numerical output is returned as a pandas DataFrame. Pynamical can then visualize this output in various ways, including with bifurcation diagrams, two-dimensional phase diagrams, three-dimensional phase diagrams, and cobweb plots.

functional_intro_to_python - A functional, Data Science focused introduction to Python

  •    HTML

The first section is an intentionally brief, functional, data science centric introduction to Python. The assumption is a someone with zero experience in programming can follow this tutorial and learn Python with the smallest amount of information possible. The sections after that, involve varying levels of difficulty and cover topics as diverse as Machine Learning and Linear Optimization to build systems and commandline tools.

react-ipython-notebook - React component for nbconvert.js

  •    Javascript

To see it in action, run $ npm start , go to http://localhost:8080, and drag an ipynb file onto the "Choose file" button.

ipynb_notedown.vim - vim plugin for editing jupyter notebook (ipynb) files through notedown

  •    Vim

Vim/Neovim plugin for editing Jupyter notebook (ipynb) files through notedown. When you open a Jupyter Notebook (*.ipynb) file, it is automatically converted from json to markdown through the notedown utility. Upon saving the file, the content is converted back to the json notebook format.

find-path - Find path by using Semantic Segmentation.

  •    Jupyter

Find Path project finds humans paths and routes, such as sidewalks, park ways, forest paths. This project implements semantic segmentation approach. It uses VGG16 pretrained model. Go to calculations folder.

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