python_intro - Jupyter notebooks in Russian

  •        2

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.



Related Projects

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-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.

lolviz - A simple Python data-structure visualization tool for lists of lists, lists, dictionaries; primarily for use in Jupyter notebooks / presentations

  •    Jupyter

By Terence Parr. See for more stuff. A simple Python data-structure visualization tool that started out as a List Of Lists (lol) visualizer but now handles arbitrary object graphs, including function call stacks! lolviz tries to look out for and format nicely common data structures such as lists, dictionaries, linked lists, and binary trees. This package is primarily for use in teaching and presentations with Jupyter notebooks, but could also be used for debugging data structures. Useful for devoting machine learning data structures, such as decision trees, as well.

numerical-linear-algebra - Free online textbook of Jupyter notebooks for fast

  •    Jupyter

This course was taught in the University of San Francisco's Masters of Science in Analytics program, summer 2017 (for graduate students studying to become data scientists). The course is taught in Python with Jupyter Notebooks, using libraries such as Scikit-Learn and Numpy for most lessons, as well as Numba (a library that compiles Python to C for faster performance) and PyTorch (an alternative to Numpy for the GPU) in a few lessons. Accompanying the notebooks is a playlist of lecture videos, available on YouTube. If you are ever confused by a lecture or it goes too quickly, check out the beginning of the next video, where I review concepts from the previous lecture, often explaining things from a new perspective or with different illustrations, and answer questions.

spark-py-notebooks - Apache Spark & Python (pySpark) tutorials for Big Data Analysis and Machine Learning as IPython / Jupyter notebooks

  •    Jupyter

This is a collection of IPython notebook/Jupyter notebooks intended to train the reader on different Apache Spark concepts, from basic to advanced, by using the Python language. If Python is not your language, and it is R, you may want to have a look at our R on Apache Spark (SparkR) notebooks instead. Additionally, if your are interested in being introduced to some basic Data Science Engineering, you might find these series of tutorials interesting. There we explain different concepts and applications using Python and R.

pelican-ipynb - Pelican plugin for blogging with Jupyter/IPython Notebooks

  •    Jupyter

Python 2.7 and 3.4 are supported. See below for additional settings in your, depending on the mode you are using.

python-cheatsheet - Basic Cheat Sheet for Python (PDF, Markdown and jupyter Notebook).

  •    Jupyter

Basic cheatsheet for Python mostly based on the book written by Al Sweigart, Automate the Boring Stuff with Python under the Creative Commons license and many other sources. Long time Pythoneer Tim Peters succinctly channels the BDFL's guiding principles for Python's design into 20 aphorisms, only 19 of which have been written down.

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

  •    Python

This is the list of published articles on 🇬🇧, 🇷🇺, and 🇨🇳. 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.

Math-of-Machine-Learning-Course-by-Siraj - Implements common data science methods and machine learning algorithms from scratch in python

  •    Jupyter

This repository was initially created to submit machine learning assignments for Siraj Raval's online machine learning course. The purpose of the course was to learn how to implement the most common machine learning algorithms from scratch (without using machine learning libraries such as tensorflow, PyTorch, scikit-learn, etc). Although that course has ended now, I am continuing to learn data science and machine learning from other sources such as Coursera, online blogs, and attending machine learning lectures at University of Toronto. Sticking to the theme of implementing machine learning algortihms from scratch, I will continue to post detailed notebooks in python here as I learn more.

aima-python - Python implementation of algorithms from Russell And Norvig's "Artificial Intelligence - A Modern Approach"

  •    Jupyter

Python code for the book Artificial Intelligence: A Modern Approach. You can use this in conjunction with a course on AI, or for study on your own. We're looking for solid contributors to help. This code requires Python 3.4 or later, and does not run in Python 2. You can install Python or use a browser-based Python interpreter such as You can run the code in an IDE, or from the command line with python -i where the -i option puts you in an interactive loop where you can run Python functions. See for instructions on setting up your own Jupyter notebook environment, or run the notebooks online with

SwiftStructures - Examples of commonly used data structures and algorithms in Swift.

  •    Swift

This project provides a framework for commonly used data structures and algorithms written in a new iOS development language called Swift. While details of many algorithms exists on Wikipedia, these implementations are often written as pseudocode, or are expressed in C or C++. With Swift now officially released, its general syntax should be familiar enough for most programmers to understand. As a developer, you should already be familiar with the basics of programming. Beyond algorithms, this project also aims to provide an alternative for learning the basics of Swift. This includes implementations of many Swift-specific features such as optionals, extensions, protocols and generics. Beyond Swift, audiences should be familiar with Singleton and Factory design patterns along with sets, arrays and dictionaries.

essentia - C++ library for audio and music analysis, description and synthesis, including Python bindings

  •    Jupyter

Essentia is an open-source C++ library for audio analysis and audio-based music information retrieval released under the Affero GPL license. It contains an extensive collection of reusable algorithms which implement audio input/output functionality, standard digital signal processing blocks, statistical characterization of data, and a large set of spectral, temporal, tonal and high-level music descriptors. The library is also wrapped in Python and includes a number of predefined executable extractors for the available music descriptors, which facilitates its use for fast prototyping and allows setting up research experiments very rapidly. Furthermore, it includes a Vamp plugin to be used with Sonic Visualiser for visualization purposes. Essentia is designed with a focus on the robustness of the provided music descriptors and is optimized in terms of the computational cost of the algorithms. The provided functionality, specifically the music descriptors included in-the-box and signal processing algorithms, is easily expandable and allows for both research experiments and development of large-scale industrial applications. If you use example extractors (located in src/examples), or your own code employing Essentia algorithms to compute descriptors, you should be aware of possible incompatibilities when using different versions of Essentia.

distiller - Neural Network Distiller by Intel AI Lab: a Python package for neural network compression research

  •    Python

Distiller is an open-source Python package for neural network compression research. Network compression can reduce the memory footprint of a neural network, increase its inference speed and save energy. Distiller provides a PyTorch environment for prototyping and analyzing compression algorithms, such as sparsity-inducing methods and low-precision arithmetic.

nbviewer - Nbconvert as a webservice (rendering ipynb to static HTML)

  •    Python

Jupyter nbviewer is the web application behind The Jupyter Notebook Viewer, which is graciously hosted by Rackspace. Run this locally to get most of the features of nbviewer on your own network.

Computer-Vision-Basics-with-Python-Keras-and-OpenCV - Full tutorial of computer vision and machine learning basics with OpenCV and Keras in Python

  •    Jupyter

This was created as part of an educational for the Western Founders Network computer vision and machine learning educational session. Note: Please check the issues on this repo if you're having problems with the notebook.

CADL - Course materials/Homework materials for the FREE MOOC course on "Creative Applications of Deep Learning w/ Tensorflow" #CADL

  •    Jupyter

This repository contains lecture transcripts and homework assignments as Jupyter Notebooks for the first of three Kadenze Academy courses on Creative Applications of Deep Learning w/ Tensorflow. It also contains a python package containing all the code developed during all three courses. The first course makes heavy usage of Jupyter Notebook. This will be necessary for submitting the homeworks and interacting with the guided session notebooks I will provide for each assignment. Follow along this guide and we'll see how to obtain all of the necessary libraries that we'll be using. By the end of this, you'll have installed Jupyter Notebook, NumPy, SciPy, and Matplotlib. While many of these libraries aren't necessary for performing the Deep Learning which we'll get to in later lectures, they are incredibly useful for manipulating data on your computer, preparing data for learning, and exploring results.

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.