Позначка: Library

# Awesome-Python-Learning

Python Learning Library

## Contents

Jobs

Programming (learning)

Roadmaps

MOOC and courses

Video

Textbooks

Books

Games for Education

Online education (others)

IT-events

Training

Project examples for Junior

Puzzles

Developer’s Tools

Articles about Software Engineering

Jobs

• [15 App Ideas to Build and Level Up your Coding Skills]
• [15 вопросов по Python: как джуниору пройти собеседование]
• [15 задач на собеседовании для программиста]
• [16-річний програміст із Черкащини — про те, як 11-класником влаштувався на роботу зі зарплатнею майже $1000 | DOU]
• [5 советов по созданию вашего резюме]
• [50+ Data Structure and Algorithms Problems from Coding Interviews – DEV Community 👩‍💻👨‍💻]
• [53 Python Interview Questions and Answers – Towards Data Science]
• [hh.ua]
• [How to write a killer Software Engineering résumé – freeCodeCamp.org – Medium]
• [Job search, venture investing & new tech products | AngelList]
• [Python програмісти – Джин]
• [Top 100 Data Structure and Algorithm Interview Questions for Java Programmers | Java67]
• [Top 75 Programming Interview Questions Answers to Crack Any Coding Job Interview | Java67]
• [What I want (and don’t want) to see on your software engineering resume]
• [Вакансії | DOU]
• [Вастрик.Инсайд #39: Войти в айти. Нужен ли диплом? Как учиться новому? Как оставаться востребованным? Есть ли жизнь после 30?]
• [Вастрик.Инсайд #46: Краткий гайд о том, как нанимать нормальных людей]
• [Де, як і скільки: аналізуємо найм джуніорів у 2019 році | DOU]
• [Как должно выглядеть резюме ИТ-специалиста: типичные ошибки глазами HR]
• [Как успешно пройти любое техническое собеседование]
• [Работа]

Programming (learning)

Roadmaps

• [My Data Science & Machine Learning, Beginner’s Learning Path | LinkedIn]
• [AI & ML дайджест #17: курсы по ML & DL, обзор популярных GAN архитектур, AI бот для ребенка | DOU]
• [Data Science Career Guide — Dataquest]
• [Data Science Career Tips Archives — Dataquest]
• [Full-Stack JavaScript in Six Weeks: A Curriculum Guide]
• [Grow]
• [Grow]
• [How to become a data scientist? – Towards Data Science]
• [How we built our first full-stack JavaScript web app in three weeks]
• [If I had to start learning Data Science again, how would I do it?]
• [omreps/programmer-competency-matrix: ENG -> RU: Матрица компетентности программиста, мой перевод]
• [Pandas Урок — чтение файлов csv, создание dataframe и фильтрация данных]
• [Programmer Competency Matrix — Sijin Joseph]
• [programmer-competency-matrix/partII.md at master · omreps/programmer-competency-matrix]
• [Python Developers Survey 2019 Results | JetBrains: Developer Tools for Professionals and Teams]
• [Quiz: Data Engineer, Data Analyst, Data Scientist — Which Role Fits You?]
• [Resources I Wish I Knew When I Started Out With Data Science]
• [Roadmap • mlcourse.ai]
• [Top 10 In-Demand programming languages to learn in 2020]
• [Warning: Your programming career – SoloLearn – Medium]
• [Дайджест материалов по трудоустройству в сфере IT]
• [Детальный план самообразования в Computer Science за 1.5 года]
• [Мапа розвитку в Data Science, або Як стати дослідником даних | DOU]
• [Программирование на Python: от новичка до профессионала]
• [Путь Python Junior-а в 2017]
• [Развивать себя]
• [Советы сеньоров: как прокачать знания junior Front-end/JavaScript | DOU]
• [Хочу стать веб-разработчиком: подробный план по изучению JavaScript]

MOOC and courses

• [[UNИX][Python-Dev] Лекция 1. Разработка ПО. Индивидуальное использование GIT – YouTube]
• [11.1 – Regular Expressions – Мичиганский университет | Coursera]
• [200+ SQL Interview Questions and Answers for Developers | Udemy]
• [6.006: Introduction to Algorithms – Massachusetts Institute of Technology]
• [Advanced Operating Systems]
• [AI For Everyone — главная | Coursera]
• [Algorithmic Toolbox | Coursera]
• [Amazon Web Services Sign-In]
• [An Introduction to Interactive Programming in Python (Part 1) – Rice University | Coursera]
• [An Introduction to Interactive Programming in Python (Part 1) — главная | Coursera]
• [AP®︎ Computer Science Principles (AP®︎ CSP) | Khan Academy]
• [App Maker Academy | LEARN]
• [Applied Machine Learning in Python — главная | Coursera]
• [Applied Machine Learning in Python | Coursera]
• [Applied Plotting, Charting & Data Representation in Python | Coursera]
• [Applied Plotting, Charting & Data Representation in Python | Coursera]
• [Applied Social Network Analysis in Python | Coursera]
• [Applied Text Mining in Python | Coursera]
• [Applied Text Mining in Python | Coursera]
• [Artificial Intelligence]
• [Artificial Intelligence for Robotics | Udacity]
• [Basics of Software Architecture & Design Patterns in Java | Udemy]
• [Become a Python Developer]
• [Become a Solution Architect: Architecture Course | Udemy]
• [Big Data Analytics in Healthcare]
• [Bootcamp – Scrimba Tutorial]
• [Bootstrap 4 Tutorial – Learn Bootstrap For Free | Scrimba]
• [Break Away: Programming And Coding Interviews | Udemy]
• [Caesar – CS50x]
• [Chat with React – Scrimba Tutorial]
• [Clean Code | LEARN]
• [Client-Server Communication]
• [Code Basics: основы программирования]
• [Codecademy]
• [CodeSkulptor]
• [Coding for Kids | Tynker]
• [Coding Interview Bootcamp Algorithms, Data Structures Course | Udemy]
• [Command Line Essentials: Git Bash for Windows | Udemy]
• [Computability, Complexity & Algorithms]
• [Computability, Complexity & Algorithms – Udacity]
• [Computer programming | Computing | Khan Academy]
• [Computer science | Computing | Khan Academy]
• [Continuous Integration and Continuous Delivery using Interactive Browser-Based Labs | Katacoda]
• [Continuous Integration with Jenkins | LEARN]
• [Convolutional Neural Networks | Coursera]
• [Course | 15.071x | edX]
• [Course | CS1301xII | edX]
• [Course Introduction: Neural Networks in JavaScript – Scrimba Tutorial]
• [Course overview]
• [Course Overview – Scrimba Tutorial]
• [Courses | edX]
• [Courses | LEARN]
• [Crash Course on Python — главная | Coursera]
• [CS 436: Distributed Computer Systems – Lecture 1 – YouTube]
• [CSS – Scrimba Tutorial]
• [Dashboard | Khan Academy]
• [Data Analysis and Visualization]
• [Data Analysis Learning Path | Springboard]
• [Data Science A-Z™: Real-Life Data Science Exercises Included | Udemy]
• [Data Science Dream Job – YouTube]
• [Data Science Interview Prep]
• [Data Science Office Hours – YouTube]
• [Data Science with R | Pluralsight]
• [Data Scientist Path: Interactive Python, SQL | Dataquest]
• [Data Structure – Part I | Udemy]
• [Data Structures and Performance | Coursera]
• [Data Structures Fundamentals]
• [Data Visualization in Python — DataCamp]
• [Data Wrangling with MongoDB]
• [Database Systems Concepts & Design]
• [DataCamp]
• [Datatypes & Typecasting – Scrimba Tutorial]
• [DBA1 : Компания Postgres Professional]
• [DBA2 : Компания Postgres Professional]
• [Deep learning на пальцах!]
• [Design of Computer Programs]
• [Design of Computer Programs – Udacity]
• [Designing RESTful APIs]
• [Developing AI Applications on Azure — главная | Coursera]
• [Differential Equations in Action]
• [Divide and Conquer, Sorting and Searching, and Randomized Algorithms | Coursera]
• [DJANGO CHANNELS 2 Tutorial (V2) – Real Time – WebSockets – Async – YouTube]
• [Docker Swarm Mode Playground | Katacoda]
• [DZone: Programming & DevOps news, tutorials & tools]
• [Easy to Advanced Data Structures | Udemy]
• [Easy to Advanced Data Structures | Udemy]
• [Exercise files]
• [Exercises on the Python Track | Exercism]
• [Explore – LeetCode]
• [Free Clean Architecture Course: Patterns, Practices, and Principles for Beginners | Pluralsight]
• [Free Software Architect Course: Developer to Architect | Pluralsight]
• [Free SOLID Course: Principles of Object Oriented Design | Pluralsight]
• [Front Matter — Alembic 1.4.2 documentation]
• [Front-End Web UI Frameworks and Tools: Bootstrap 4 — главная | Coursera]
• [Full Stack Foundations]
• [Git Playground | Katacoda]
• [Git Started with GitHub | Udemy]
• [Graph Search, Shortest Paths, and Data Structures — главная | Coursera]
• [Graph Search, Shortest Paths, and Data Structures | Coursera]
• [Greedy Algorithms, Minimum Spanning Trees, and Dynamic Programming | Coursera]
• [Grok Learning]
• [Grokking the System Design Interview – Learn Interactively]
• [GT – Refresher – Advanced OS]
• [Harvard CS109 Data Science Course, Resources Free and Online]
• [High Performance Computer Architecture]
• [High Performance Computing]
• [HTTP & Web Servers]
• [HTTP & Web Servers – Udacity]
• [https://scratch.mit.edu/projects/374672500/editor]
• [Improving Deep Neural Networks: Hyperparameter tuning, Regularization and Optimization | Coursera]
• [Inferential Statistical Analysis with Python — главная | Coursera]
• [Input and output — Intro to Interviewing — Grasshopper]
• [Intel® Edge AI Fundamentals with OpenVINO™]
• [Intro to Algorithms – Udacity]
• [Intro to Algorithms | Udacity]
• [Intro to Artificial Intelligence – Udacity]
• [Intro to Backend – Udacity]
• [Intro to Data Analysis | Udacity]
• [Intro to Data Science]
• [Intro to Data Science]
• [Intro to Data Structures and Algorithms]
• [Intro to Deep Learning with PyTorch]
• [Intro to Descriptive Statistics]
• [Intro to DevOps]
• [Intro To Dynamic Programming – Coding Interview Preparation | Udemy]
• [Intro To Dynamic Programming – Coding Interview Preparation | Udemy]
• [Intro to Flask Series – YouTube]
• [Intro to Hadoop and MapReduce]
• [Intro to Inferential Statistics]
• [Intro to Progressive Web Apps]
• [Intro to Relational Databases]
• [Intro to the Design of Everyday Things]
• [Intro to Theoretical Computer Science]
• [Introduction – Scrimba Tutorial]
• [Introduction to Computational Thinking and Data Science | edX]
• [Introduction to Computer Vision]
• [Introduction to D3 – Scrimba Tutorial]
• [Introduction to Data Science in Python | Coursera]
• [Introduction to Data Science in Python | Coursera]
• [Introduction to Dynamical Systems and Chaos]
• [Introduction to Graduate Algorithms]
• [Introduction to GraphQL | GraphQL]
• [Introduction to JavaScript | Codecademy]
• [Introduction to Machine Learning – Online Course | DataCamp]
• [Introduction to Machine Learning Course | Udacity]
• [Introduction to Operating Systems]
• [Introduction to Operating Systems – Udacity]
• [Introduction to Python Programming – Udacity]
• [Introduction to Python Programming | Udacity]
• [Introduction To Python Programming | Udemy]
• [Introduction to Scripting in Python | Coursera]
• [IT Курсы программирования онлайн – обучение программированию, видео уроки | ITVDN]
• [JavaScript ES6 Intro – Scrimba Tutorial]
• [Kaggle Mercedes и кросс-валидация / Блог компании Open Data Science / Хабр]
• [Katacoda – Interactive Learning Platform for Software Engineers]
• [Katacoda – Interactive Learning Platform for Software Engineers]
• [Khan Academy | Free Online Courses, Lessons & Practice]
• [Knowledge-Based AI: Cognitive Systems]
• [Learn | freeCodeCamp.org]
• [Learn CI/CD with Jenkins using Interactive Browser-Based Labs | Katacoda]
• [Learn CSS Animations]
• [Learn Data Analysis using Pandas and Python (Module 2/3) | Udemy]
• [Learn Data Structures and Algorithms: Ace the Coding Interview | Udemy]
• [Learn Data Visualization Tutorials | Kaggle]
• [Learn Git Version Control using Interactive Browser-Based Labs | Katacoda]
• [Learn How to Code | Codecademy]
• [Learn Intermediate Machine Learning Tutorials | Kaggle]
• [Learn Intro to Machine Learning Tutorials | Kaggle]
• [Learn Kubernetes using Interactive Browser-Based Labs | Katacoda]
• [Learn Machine Learning using Interactive Browser-Based Labs | Katacoda]
• [Learn Pandas Tutorials | Kaggle]
• [Learn Python – Full Course for Beginners [Tutorial] – YouTube]
• [Learn Python 3.6 for Total Beginners | Udemy]
• [Learn Python for Data Science, Structures, Algorithms, Interviews | Udemy]
• [Learn Python for Data Structures, Algorithms & Interviews | Udemy]
• [Learn Python: Build a Virtual Assistant | Udemy]
• [Learn Serverless and Functions/FaaS Technologies using Interactive Browser-Based Labs | Katacoda]
• [Learn SQL | Codecademy]
• [Learn to Code with Interactive Tutorials | Scrimba]
• [Learn to Program: Crafting Quality Code — главная | Coursera]
• [Learn to Program: The Fundamentals — главная | Coursera]
• [Lecture 1: Administrivia; Introduction; Analysis of Algorithms, Insertion Sort, Mergesort | Video Lectures | Introduction to Algorithms (SMA 5503) | Electrical Engineering and Computer Science | MIT OpenCourseWare]
• [Linear Algebra Refresher Course]
• [Livestreams | Codecademy]
• [Machine Learning]
• [Machine Learning A-Z (Python & R in Data Science Course) | Udemy]
• [Machine Learning Course Outline | Course Outline | CSMM.102x Courseware | edX]
• [Machine Learning Foundations: A Case Study Approach | Coursera]
• [Machine Learning in Python (Data Science and Deep Learning) | Udemy]
• [Machine Learning Interview Preparation]
• [Machine Learning with TensorFlow on Google Cloud Platform | Coursera]
• [Machine Learning: Unsupervised Learning]
• [Master Object Oriented Programming Concepts | Udemy]
• [Master Python for Data Science]
• [Mathematics for Computer Science | Electrical Engineering and Computer Science | MIT OpenCourseWare]
• [Mathematics for Computer Science | Electrical Engineering and Computer Science | MIT OpenCourseWare]
• [Microsoft: Our new free Python programming language courses are for novice AI developers | ZDNet]
• [MIT 6.042J Mathematics for Computer Science]
• [Neural Networks and Deep Learning — главная | Coursera]
• [Node.js Playground | Katacoda]
• [Nonlinear Dynamics: Mathematical and Computational Approaches]
• [Open Data Science (ODS.ai)]
• [Open Data Science (ODS.ai)]
• [Open Machine Learning Course mlcourse.ai • mlcourse.ai]
• [ossu/computer-science: Path to a free self-taught education in Computer Science!]
• [Practical Deep Learning for Coders, v3 | fast.ai course v3]
• [Probabilistic Graphical Models 1: Representation | Coursera]
• [Programming Languages]
• [PY4E – Python for Everybody]
• [Python – OOP | Udemy]
• [Python – YouTube]
• [Python | CoderNet]
• [Python 3, BBC Microbit, and MicroPython Bootcamp | Udemy]
• [Python 3.4 Programming Tutorials – YouTube]
• [Python Basics — главная | Coursera]
• [Python for Big Data Analytics – Edureka]
• [Python for Data Science | edX]
• [Python for Data Science | edX]
• [Python OOP : Four Pillars of OOP in Python 3 for Beginners | Udemy]
• [Python OOP : Four Pillars of OOP in Python 3 for Beginners | Udemy]
• [Python Playground | Katacoda]
• [Python Programming – Build a Reconnaissance Scanner | Udemy]
• [Python Programming – YouTube]
• [Python Programming for Beginners – Learn in 100 Easy Steps | Udemy]
• [Python Programming: A Concise Introduction — главная | Coursera]
• [Python Track | Exercism]
• [Python Tutorials – YouTube]
• [Python Variables]
• [Python: Object Oriented Programming | Udemy]
• [python: Online Courses, Training and Tutorials on LinkedIn Learning]
• [Quickstart for Cloud SQL for PostgreSQL  |  Google Cloud]
• [R Language Playground | Katacoda]
• [React – Scrimba Tutorial]
• [React & Django TUTORIAL Integration // REACTify Django – YouTube]
• [React Tutorial: Learn React JS For Free | Scrimba]
• [Reading: Welcome | Welcome | ALGS201x Courseware | edX]
• [Real-Time Analytics with Apache Storm]
• [Regular Expressions Intro – Scrimba Tutorial]
• [Responsive Images]
• [Rock Paper Scissors – Python Tutorial | Udemy]
• [RPA: Automation Anywhere: Example: Ticket Processing – YouTube]
• [Scalable Microservices with Kubernetes]
• [Scipy Lecture Notes — Scipy lecture notes]
• [Self-Driving Fundamentals: Featuring Apollo]
• [Sequence Models | Coursera]
• [Shortest Paths Revisited, NP-Complete Problems and What To Do About Them | Coursera]
• [Software Architecture & Design]
• [Software Development Methodologies | LEARN]
• [Software Development Processes and Methodologies | Coursera]
• [Software Testing Introduction (RUS) | LEARN]
• [Spark]
• [SQL for Data Analysis | Udacity]
• [Stanford Engineering Everywhere | CS229 – Machine Learning]
• [Story by Data – YouTube]
• [Structuring Machine Learning Projects | Coursera]
• [TensorFlow Getting Started using Interactive Browser-Based Labs | Katacoda]
• [Textbook | Calculus Online Textbook | MIT OpenCourseWare]
• [The Bits and Bytes of Computer Networking | Coursera]
• [The Ultimate GIT 5-day Challenge | Udemy]
• [Tick Tock, Tick Tock — Етап 9 — Stepik]
• [Top Coding Interview Questions (Essential to Getting Hired) | Udemy]
• [Try Django 1.11 // Python Web Development | Udemy]
• [Try DJANGO Tutorial Series – YouTube]
• [Try DJANGO TUTORIAL Series (v2.2) // PYTHON Web Development with Django version 2.2 – YouTube]
• [TypeScript: Introduction – Scrimba Tutorial]
• [Ubuntu Playground | Katacoda]
• [UI design – Scrimba Tutorial]
• [Using Databases with Python | Coursera]
• [Version Control with Git]
• [Video | Version control with Git | VCG Courseware | LEARN]
• [Video Lectures | Introduction to Algorithms (SMA 5503) | Electrical Engineering and Computer Science | MIT OpenCourseWare]
• [Video Lectures | Introduction to Computer Science and Programming | Electrical Engineering and Computer Science | MIT OpenCourseWare]
• [Video Lectures | Mathematics for Computer Science | Electrical Engineering and Computer Science | MIT OpenCourseWare]
• [Visual Studio Code Playground | vscode | Katacoda]
• [Web Application & Software Architecture 101 – Learn Interactively]
• [Web Design, Business, Technology Classes and Immersives | General Assembly]
• [Web Tooling & Automation]
• [WebDriver | LEARN]
• [Web-Services Introduction | LEARN]
• [Website Performance Optimization]
• [Week 2 | Week 2 | CS50 Courseware | edX]
• [Welcome 👋 to Vue.js! – Scrimba Tutorial]
• [Алгоритмы | Coursera]
• [Алгоритмы на Python 3. Лекция №1 – YouTube]
• [Алгоритмы, часть I | Coursera]
• [Алгоритмы, часть I | Coursera]
• [Алгоритмы, часть II | Coursera]
• [Алгоритмы, часть II | Coursera]
• [Алексей Савватеев ” Математический анализ. Анонс” – YouTube]
• [Аналіз даних та статистичне виведення на мові R | Prometheus]
• [Бесплатное учебное руководство по теме “NumPy” — Deep Learning Prerequisites: The Numpy Stack in Python | Udemy]
• [Бесплатное учебное руководство по теме “Python” — Learn Python 3.6 for Total Beginners | Udemy]
• [Введение в математическое мышление | Coursera]
• [Введение в математическое мышление | Coursera]
• [Введение в науку о данных | Coursera]
• [Введение в программирование с MATLAB | Coursera]
• [Візуалізація даних | Prometheus]
• [Высшая математика для заочников и не только]
• [Глубокое обучение | Coursera]
• [Глубокое обучение | Coursera]
• [Интерактивные курсы — HTML Academy]
• [Інформація про курс CS50 | Prometheus]
• [Ключевые аспекты разработки на Python – Курсы по программированию]
• [Корисні ресурси для програміста (оновив 11 травня 2020) | DOU]
• [Курсы по программированию]
• [Машинне навчання | Prometheus]
• [Машинное обучение | Coursera]
• [Машинное обучение | Coursera]
• [Машинное обучение | Coursera]
• [Машинное обучение для людей :: Разбираемся простыми словами :: Блог Вастрик.ру]
• [Начало]
• [Нейронные сети и глубокое обучение | Coursera]
• [Онлайн-курсы — когда угодно, где угодно | Udemy]
• [Онлайн-курсы Computer Science Center]
• [Основи програмування CS50 2019 | Prometheus]
• [Основы React.js]
• [Основы компьютерных вычислений | Coursera]
• [Открытый курс машинного обучения. Тема 1. Первичный анализ данных с Pandas / Блог компании Open Data Science / Хабр]
• [Открытый курс машинного обучения. Тема 2: Визуализация данных c Python / Блог компании Open Data Science / Хабр]
• [Открытый курс машинного обучения. Тема 3. Классификация, деревья решений и метод ближайших соседей / Open Data Science corporate blog / Habr]
• [Открытый курс машинного обучения. Тема 3. Классификация, деревья решений и метод ближайших соседей / Блог компании Open Data Science / Хабр]
• [Панель курсів | Prometheus]
• [Практическое компьютерное обучение | Coursera]
• [Прикладная наука о данных с Python | Coursera]
• [Программирование мобильных приложений для портативных систем на базе Android: Часть 1 | Coursera]
• [Программирование мобильных приложений для портативных систем на базе Android: Часть 2 | Coursera]
• [Программирование на Python | Coursera]
• [Профессиональная сертификация ‘Google IT Automation with Python’ | Coursera]
• [Разведочный анализ данных | Coursera]
• [Разработка и проектирование адаптивных веб-сайтов | Coursera]
• [Розробка та аналіз алгоритмів. Частина 1 | Prometheus]
• [Скринкаст по Angular]
• [Скринкаст по Gulp]
• [Скринкаст по Node.JS]
• [Скринкаст по Webpack]
• [Современный учебник JavaScript]
• [Структуры данных в Python | Coursera]
• [Структуры и алгоритмы данных | Coursera]
• [Теория вероятностей для начинающих | Coursera]
• [Теория игр | Coursera]
• [Уроки по Python для начинающих и программистов ~ PythonRu]
• [Учимся программировать: основы | Coursera]
• [Функции – Московский физико-технический институт, Mail.Ru Group & ФРОО | Coursera]
• [Язык программирования JavaScript]
• [🤖 Интерактивные эксперименты с машинным обучением на TensorFlow | DOU]

Video

• [6.824 Lecture 1 – YouTube]
• [62 лучших видео для тех, кто хочет углубить знания в JavaScript]
• [AlSweigart – Twitch]
• [Berkeley AI Materials]
• [Build a Data Analysis Library from Scratch in Python – YouTube]
• [Building a keylogger using Python + Pynput – YouTube]
• [Compilers with Alex Aiken – YouTube]
• [Computer Science 162 (Fall 2010) – Lecture 1 – YouTube]
• [Computer Science 61A, 001 – Spring 2011 : Free Movies : Free Download, Borrow and Streaming : Internet Archive]
• [CS144 Fall 2013, Video 4-0: Congestion Control – YouTube]
• [Essence of linear algebra – YouTube]
• [Git – Lecture 0 – CS50’s Web Programming with Python and JavaScript – YouTube]
• [Golovach Courses – YouTube]
• [L01 Functional Programming | UC Berkeley CS 61A, Spring 2010 – YouTube]
• [Steven Skiena – Algorithms]
• [Tech Debates Webinar | Ruby vs Python – Zoom]
• [UC Berkeley CS professor Brian Harvey]
• [UCBerkeley Course Computer Science 186 : Free Download, Borrow, and Streaming : Internet Archive]
• [Video Lectures | Structure and Interpretation of Computer Programs | Electrical Engineering and Computer Science | MIT OpenCourseWare]
• [Изучение программирования. SQL – YouTube]
• [Лучшие Youtube-каналы для Frontend-разработчика]
• [Лучший видеокурс по сетевым технологиям]
• [Огромный видеокурс по основам JavaScript от freeCodeCamp]

Textbooks

• [100 days of algorithms — Medium]
• [30 Helpful Python Snippets That You Can Learn in 30 Seconds or Less]
• [97-things-every-programmer-should-know/ru/thing_01 at master · 97-things/97-things-every-programmer-should-know]
• [All the basics of Python classes – Level Up Coding]
• [BeginnersGuide/Programmers – Python Wiki]
• [Calculating Streaks in Pandas]
• [Cheat Sheet – Google Таблиці]
• [Cheat Sheet of Machine Learning and Python (and Math) Cheat Sheets]
• [Deep Learning]
• [DevDocs API Documentation]
• [DZone Big Data]
• [Front-end Developer Handbook 2019 – Learn the entire JavaScript, CSS and HTML development practice!]
• [Google’s Python Class  |  Python Education  |  Google Developers]
• [Learn DS & Algorithms | Programiz]
• [List of Keywords in Python Programming]
• [Manning | Catalog]
• [Manning | Deep Learning with Python, Second Edition]
• [Manning | Get Programming]
• [Manning | liveBook]
• [Manning | Making Sense of Edge Computing]
• [Manning | The Quick Python Book, Third Edition]
• [MLOps: Continuous delivery and automation pipelines in machine learning]
• [NumPy Tutorial: Data Analysis with Python — Dataquest]
• [Python – Все для студента]
• [Python | CoderNet]
• [Python 3 для начинающих и чайников – уроки программирования]
• [Python Pandas Tutorial – Tutorialspoint]
• [Python Tricks 101🐍 – HackerNoon.com – Medium]
• [Python Tutorial | Learn Python For Data Science]
• [Python Tutorial for Beginners: Learn Python Programming in 7 Days]
• [Python на Хабре / Хабр]
• [Python/Учебник Python 3.1 — Викиучебник]
• [Scipy Tutorial: Vectors and Arrays (Linear Algebra) – DataCamp]
• [skromnitsky/awesome: 😎 Awesome lists about all kinds of interesting topics]
• [Stencil Computations with Numba — Dask Examples documentation]
• [The Ultimate Guide to Python: How to Go From Beginner to Pro]
• [Tutorials – Online Data Analysis & Interpretation | DataCamp]
• [www.ПЕРВЫЕ ШАГИ.ru :: Шаг 38 – PL/SQL – вводный курс]
• [Вычислительная Фотография :: Будущее фотографии — это код :: Блог Вастрик.ру]
• [Добавляем параллельные вычисления в Pandas / Хабр]
• [Инструменты Python: лучшая шпаргалка для начинающих]
• [Нескучный туториал по NumPy / Хабр]
• [Оглавление — Problem Solving with Algorithms and Data Structures]
• [Основы языка программирования Python за 10 минут / Хабр]
• [Программирование на языке PL/SQL под базы данных Oracle]
• [Руководство Oracle PL/SQL]
• [Содержание E:\DEV\Programming\]
• [Сохраните в закладках эту статью, если вы новичок в Python (особенно если изучаете Python сами) / Хабр]

Books

• [10 лучших книг по программированию по мнению Reddit]
• [15 лучших книг по программированию на Python – Kompot Journal]
• [3 лучших книги по объектно-ориентированному программированию]
• [3 лучших книги по объектно-ориентированному программированию]
• [5 отличных англоязычных книг по теоретическому Computer Science]
• [6 бесплатных книг по алгоритмам в программировании]
• [6.042J Complete course notes]
• [8 книг по компьютерным сетям]
• [Architectural Styles and the Design of Network-based Software Architectures]
• [Build your first app  |  Android Developers]
• [Building Blocks for Theoretical Computer Science]
• [Computer Networking: a Top Down Approach]
• [Data-Oriented Design]
• [Distributed Systems: Principles and Paradigms]
• [Dive Into Python 3]
• [dmitryrubtsov/Python-for-Data-Science: Education]
• [Domain Driven Design Quickly]
• [Elements of Statistical Learning: data mining, inference, and prediction. 2nd Edition.]
• [Eloquent JavaScript]
• [holoviz/hvplot: A high-level plotting API for pandas, dask, xarray, and networkx built on HoloViews]
• [How to Design Programs]
• [How to not get caught while web scraping? – Data Driven Investor – Medium]
• [HTML5 и CSS3. Веб-разработка по стандартам нового поколения – Хоган Брайан – Google Книги]
• [Introduction – Выразительный Javascript]
• [Introduction to Computing: Explorations in Language, Logic, and Machines]
• [Introduction to Linear Algebra, 5th Edition]
• [Introduction to Programming in Java · Computer Science]
• [JQuery]
• [Learn Python – Free Interactive Python Tutorial]
• [Learn Python in Y Minutes]
• [Learn Python the Hard Way]
• [Python Documentation contents — Python 3.8.2 documentation]
• [R Tutorial for Beginners: Learning R Programming]
• [Springer has released 65 Machine Learning and Data books for free]
• [Teach Yourself Computer Science]
• [The Functional Art: An Introduction to Information Graphics and Visualization: The Functional Art]
• [The HoTT Book | Homotopy Type Theory]
• [The Python Standard Library — Python 3.8.2 documentation]
• [TheoryOfComputation.dvi]
• [Think Python]
• [tr22.pdf]
• [Tutorial – Learn Python in 10 minutes – Stavros’ Stuff]
• [Tutorials — pandas 1.0.3 documentation]
• [unmaintainable code : Java Glossary]
• [You-Dont-Know-JS/README.md at 2nd-ed · getify/You-Dont-Know-JS]
• [You-Dont-Know-JS/README.md at 2nd-ed · getify/You-Dont-Know-JS]
• [Большая книга веб-дизайна – Терри Фельке-Моррис – Google книги]
• [Каталог книг | VK]
• [Категория:Программирование — Википедия]
• [Книга «Python для сложных задач: наука о данных и машинное обучение» / Блог компании Издательский дом «Питер» / Хабр]
• [Лучший самоучитель по Java для начинающих и продвинутых]
• [Математические основы анализа данных: подборка материалов по вузовской математике]
• [Обложка — A Byte Of Python — русский перевод]
• [Оглавление — Problem Solving with Algorithms and Data Structures]
• [Перевод документации Python 3.x]
• [Помнить все: делимся лучшей шпаргалкой по Python]
• [Топ 10 самых популярных книг по программированию]
• [Учебник по NumPy – Визуализация примеров для быстрого изучения]

Games for Education

• [10 мобильных приложений, которые научат вас программировать]
• [12 бесплатных ресурсов для обучения программированию в игровой форме]
• [House Prices: Advanced Regression Techniques | Kaggle]
• [Learn Git Branching]
• [Play CodeCombat Levels – Learn Python, JavaScript, and HTML | CodeCombat]
• [The Python Challenge]
• [Titanic: Machine Learning from Disaster | Kaggle]

Online education (others)

• [arXiv.org e-Print archive]
• [Code Basics: основы программирования на Javascript]
• [CoderNet Портал для помощи программистам | CoderNet]
• [cs184/284a]
• [CS61C Spring 2015: Great Ideas in Computer Architecture (Machine Structures)]
• [Free JavaScript Course: Code School JavaScript Road Trip Part 1 | Pluralsight]
• [kanaka/mal: mal – Make a Lisp]
• [Learn Basic JavaScript: Declare JavaScript Variables | freeCodeCamp.org]
• [Machine Learning is Fun! – Adam Geitgey – Medium]
• [Machine Learning Mastery]
• [new fast.ai course: A Code-First Introduction to Natural Language Processing · fast.ai]
• [OOP Concept Tutorial in Java – Object Oriented Programming | Java67]
• [Operating Systems: Three Easy Pieces]
• [Programming — Towards Data Science]
• [PyVideo.org]
• [Skiena’s Audio Lectures]
• [Solve Easy Unpack – Py.CheckiO]
• [Web  |  Google Developers]
• [Why every Data Scientist should use Dask? – Towards Data Science]
• [Открытый курс машинного обучения. Тема 1. Первичный анализ данных с Pandas / Хабр]
• [Упражнения по SQL]

IT-events

• [Календар IT-подій в Києві | DOU]

Training

• [Day 0: Hello, World. | HackerRank]
• [Problems – LeetCode]
• [Skill Assessment]
• [Solve Python | HackerRank]
• [Solve Python | HackerRank]

Project examples for Junior

• [(1) Python]
• [10 Data Science Projects — Dataquest]
• [10 Great Programming Projects to Improve Your Resume and Learn to Program]
• [13 Project Ideas for Intermediate Python Developers — Real Python]
• [5 Cool Python Project Ideas For Inspiration | Hacker Noon]
• [6 Python Projects For Beginners | Codementor]
• [Building a Simple Chatbot from Scratch in Python (using NLTK)]
• [CodeProject – For those who code]
• [karan/Projects: A list of practical projects that anyone can solve in any programming language.]
• [latest Python project topics and ideas with source code for final year projects – kashipara]
• [Machine Learning Projects | Data Science Projects with Example]
• [Martyr2’s Mega Project Ideas List! – Share Your Project | Dream.In.Code]
• [Neural Network Embedding Recommendation System | Kaggle]
• [Python Project Ideas for Final Year, Python Project Help]
• [Top Python Projects | Easy, Intermediate And Advanced Python Projects | Edureka]
• [Tutorial 2 – Making it interesting — BeeWare 0.3.0 documentation]

Puzzles

• [About – Project Euler]
• [CodeKata]
• [Coderbyte | Code Screening, Challenges, & Interview Prep]
• [Problem set @ Timus Online Judge]
• [Programming Praxis | A collection of etudes, updated weekly, for the education and enjoyment of the savvy programmer]
• [The Daily WTF: Curious Perversions in Information Technology]
• [Задачи для программистов, ответы на задания различной сложности]
• [Мозговой фитнес. Актуальные задачи для прокачки программистских скиллов]
• [Решаем задачи на одномерные и двумерные массивы]

Developer’s Tools

• [(Tutorial) Web Scraping With Python: Beautiful Soup – DataCamp]
• [| fastai]
• [10 Best CSS Frameworks For Frontend Developers in 2020 – GeeksforGeeks]
• [10 лучших материалов для изучения Django]
• [20 short tutorials all data scientists should read (and practice) – Data Science Central]
• [7 Steps to Mastering Machine Learning With Python]
• [A Beginner’s Introduction to Python Web Frameworks : Python]
• [A successful Git branching model » nvie.com]
• [ageitgey/face_recognition: The world’s simplest facial recognition api for Python and the command line]
• [Algorithms – Algorithmia]
• [All Tools — PyViz 0.0.1 documentation]
• [An Intro to Git and GitHub for Beginners (Tutorial)]
• [Apache против Nginx: практические соображения]
• [ARCore – Google Developers]
• [Are you still using Pandas for big data? – Towards Data Science]
• [ArtVk & Bugtrack: Задачи по базам данных. Решение задач по SQL [1]]
• [Awesome Python | LibHunt]
• [awesome-vscode | 🎨 A curated list of delightful VS Code packages and resources.]
• [Azure Machine Learning SDK for Python – Azure Machine Learning Python | Microsoft Docs]
• [Batch convert images to PDF with Python by using Pillow or img2pdf | Solarian Programmer]
• [benfred/py-spy: Sampling profiler for Python programs]
• [Better Code Hub]
• [Big data sets available for free – Data Science Central]
• [Build and deploy your first machine learning web app]
• [Build and run a Python app in a container]
• [Building A Blog Application With Django | Django Central]
• [Building a Microservice in Python – Sonu Sharma – Medium]
• [Caffe | Installation]
• [CAL board – Agile board – Jira]
• [Catalog of Patterns of Enterprise Application Architecture]
• [Chapter 1. Getting to know Redis – Redis in Action]
• [Choosing the right estimator — scikit-learn 0.22.2 documentation]
• [CLI Setup – NativeScript Docs]
• [Cloud Shell]
• [Codacy Onboarding]
• [Codecov]
• [CodePen: Build, Test, and Discover Front-end Code.]
• [colorama · PyPI]
• [Conda Cheat Sheet – Kapeli]
• [Control Room | Home | Automation Anywhere]
• [Coveralls – Test Coverage History & Statistics]
• [CRAN – Package rattle]
• [create-graphql-server — instantly scaffold a GraphQL server]
• [Dash Bootstrap Components]
• [Dash for Beginners – DataCamp]
• [Dashboard : skromnitsky : PythonAnywhere]
• [Dashboard · WakaTime]
• [Dask + Numba for Efficient In-Memory Model Scoring – Capital One Tech – Medium]
• [Data Science with Python: Intro to Data Visualization with Matplotlib]
• [Data: Querying, Analyzing and Downloading: The GDELT Project]
• [Dataset Search]
• [Datasets for Data Mining and Data Science]
• [Django ORM. Добавим сахарку / Хабр]
• [Django в примерах · GitBook (Legacy)]
• [Django на русском]
• [Django Руководство часть 11: Разворачивание сайта на сервере – Изучение веб-разработки | MDN]
• [Download · Bootstrap]
• [Download All Free Textbooks from Springer using Python]
• [Download jQuery | jQuery]
• [Download RequireJS]
• [ekzhu/datasketch: MinHash, LSH, LSH Forest, Weighted MinHash, HyperLogLog, HyperLogLog++, LSH Ensemble]
• [emmetio/emmet-atom: Emmet support for Atom]
• [Erotemic/ubelt: A Python utility belt containing simple tools, a stdlib like feel, and extra batteries. Hashing, Caching, Timing, Progress, and more made easy!]
• [Exploring your data with just 1 line of Python – Towards Data Science]
• [eyaltrabelsi/pandas-log: The goal of pandas-log is to provide feedback about basic pandas operations. It provides simple wrapper functions for the most common functions that add additional logs]
• [facebook/create-react-app: Set up a modern web app by running one command.]
• [facebookresearch/detectron2: Detectron2 is FAIR’s next-generation platform for object detection and segmentation.]
• [facebookresearch/DrQA: Reading Wikipedia to Answer Open-Domain Questions]
• [facebookresearch/faiss: A library for efficient similarity search and clustering of dense vectors.]
• [facebookresearch/fastMRI: A large-scale dataset of both raw MRI measurements and clinical MRI images]
• [facebookresearch/habitat-api: A modular high-level library to train embodied AI agents across a variety of tasks, environments, and simulators.]
• [facebookresearch/LAMA: LAnguage Model Analysis]
• [facebookresearch/nevergrad: A Python toolbox for performing gradient-free optimization]
• [facebookresearch/pytext: A natural language modeling framework based on PyTorch]
• [facebookresearch/pytorch_GAN_zoo: A mix of GAN implementations including progressive growing]
• [facebookresearch/VideoPose3D: Efficient 3D human pose estimation in video using 2D keypoint trajectories]
• [facebookresearch/visdom: A flexible tool for creating, organizing, and sharing visualizations of live, rich data. Supports Torch and Numpy.]
• [facebookresearch/vizseq: An Analysis Toolkit for Natural Language Generation (Translation, Captioning, Summarization, etc.)]
• [Getting started — Flexx 1.0 documentation]
• [Getting started — HiPlot 0.1.9.post2 documentation]
• [Getting started — pandas 1.0.3 documentation]
• [Getting started — SciPy.org]
• [Getting started with Django | Django]
• [Getting started with PyMC3 — PyMC3 3.8 documentation]
• [github/hub: A command-line tool that makes git easier to use with GitHub.]
• [Graphene-Python]
• [great-expectations/great_expectations: Always know what to expect from your data.]
• [gto76/python-cheatsheet: Comprehensive Python Cheatsheet]
• [GUI (графический интерфейс пользователя) | Python 3 для начинающих и чайников]
• [GuiProgramming – Python Wiki]
• [Hello, App Center]
• [How to deploy ML models using Flask + Gunicorn + Nginx + Docker]
• [How to Get a Job with Python – Towards Data Science]
• [How to Install and Run Hadoop on Windows for Beginners – Data Science Central]
• [How to Update All Python Packages | ActiveState]
• [Installation — Kivy 1.11.1 documentation]
• [Installation — pyglet v1.5.0]
• [Integrating Summernote WYSIWYG Editor in Django  | Django Central]
• [interpretml/interpret: Fit interpretable machine learning models. Explain blackbox machine learning.]
• [Introducing Bamboolib — a GUI for Pandas – Towards Data Science]
• [Introducing GitFlow]
• [ipinfo.io/json]
• [ironmussa/Optimus at develop-3.0]
• [japronto/1_hello.md at master · squeaky-pl/japronto]
• [jaybaird/python-bloomfilter: Scalable Bloom Filter implemented in Python]
• [jazzband/pip-tools: A set of tools to keep your pinned Python dependencies fresh.]
• [Jetware – aise / tensorflow18_keras21_python36_cpu_notebook – 180509 appliance]
• [jiffyclub/snakeviz: An in-browser Python profile viewer]
• [JS Bin – Collaborative JavaScript Debugging]
• [Keras Cheat Sheet: Neural Networks in Python – DataCamp]
• [keyboard-shortcuts-windows.pdf]
• [knockknock/README.md at master · huggingface/knockknock]
• [Laravel Nova – Beautifully-designed administration panel for Laravel]
• [localhost:54321/callback?code=b7e3a07c1695c5b58b6d]
• [Machine Learning]
• [main]
• [marcotcr/lime: Lime: Explaining the predictions of any machine learning classifier]
• [MIT App Inventor 2]
• [More Itertools — more-itertools 8.2.0 documentation]
• [mouradmourafiq/pandas-summary: An extension to pandas dataframes describe function.]
• [Native mobile apps with Angular, Vue.js, TypeScript, JavaScript – NativeScript]
• [nicolaskruchten/pivottable: Open-source Javascript Pivot Table (aka Pivot Grid, Pivot Chart, Cross-Tab) implementation with drag’n’drop.]
• [NLTK Data]
• [NoSQL Databases List by Hosting Data – Updated 2020]
• [Numba: A High Performance Python Compiler]
• [Numpy and Scipy Documentation — Numpy and Scipy documentation]
• [NumPy в Python. Часть 1 / Хабр]
• [Optimus/README.md at master · ironmussa/Optimus]
• [Over 150 of the Best Machine Learning, NLP, and Python Tutorials I’ve Found]
• [Overview — Matplotlib 3.2.1 documentation]
• [Overview — NumPy v1.19.dev0 Manual]
• [PageSpeed Insights]
• [pallets/werkzeug: The comprehensive WSGI web application library.]
• [Pivot Demo From Local CSV]
• [PivotTable.js]
• [Plotting Google Sheets data in Python with Folium – Towards Data Science]
• [Plugin Status · WakaTime]
• [Polymer Project]
• [Popper – Tooltip & Popover Positioning Engine]
• [PostgreSQL : Документация : Компания Postgres Professional]
• [Prisma – Database tools for modern application development]
• [Projects – Home]
• [pydqc/README.md at master · SauceCat/pydqc]
• [PyQt5 book with a foreword by the creator of PyQt]
• [Pythia’s Documentation — Pythia 0.3 documentation]
• [Python 3.8 documentation — DevDocs]
• [Python Extension Packages for Windows – Christoph Gohlke]
• [Python Frameworks Comparison: How to Choose the Best for Web Development]
• [Python в три ручья: работаем с потоками (часть 1) | GeekBrains – образовательный портал]
• [pytorch3d/INSTALL.md at master · facebookresearch/pytorch3d]
• [Qt Designer Download for Windows and Mac]
• [Quick Start | GatsbyJS]
• [Quick Start · gulp.js]
• [Quickstart for Python/WSGI applications — uWSGI 2.0 documentation]
• [Quickstart tutorial — NumPy v1.19.dev0 Manual]
• [R Packages – RStudio]
• [RaRe-Technologies/bounter: Efficient Counter that uses a limited (bounded) amount of memory regardless of data size.]
• [React — JavaScript-бібліотека для створення користувацьких інтерфейсів]
• [Roundup of Python NLP Libraries – NLP-FOR-HACKERS]
• [samuelhwilliams/Eel: A little Python library for making simple Electron-like HTML/JS GUI apps]
• [SciPy — SciPy v1.4.1 Reference Guide]
• [SciPy.org — SciPy.org]
• [Settings | Account · WakaTime]
• [shaypal5/cachier: Persistent, stale-free, local and cross-machine caching for Python functions.]
• [sindresorhus/awesome: 😎 Awesome lists about all kinds of interesting topics]
• [skromnitsky/My_Projects]
• [Speech Recognition – Speech to Text in Python using Google API, Wit.AI, IBM, CMUSphinx]
• [Speed Up your Algorithms Part 2— Numba – Towards Data Science]
• [Speeding up your Algorithms Part 4— Dask – Towards Data Science]
• [Stop Worrying and Create your Deep Learning Server in 30 minutes]
• [streamlit/streamlit: Streamlit — The fastest way to build custom ML tools]
• [Superbird11/ranges: Continuous Range, RangeSet, and RangeDict data structures for Python]
• [The 30 Best Python Libraries and Packages for Beginners]
• [The Basics of Data Visualisation with Python – Towards Data Science]
• [The Big Bad NLP Database: Access Nearly 300 Datasets]
• [The Coolest Data Science And Machine Learning Tool Companies Of The 2020 Big Data 100]
• [The Most Underrated Python Packages – Towards Data Science]
• [The Super Duper NLP Repo: 100 Ready-to-Run Colab Notebooks]
• [Tkinter. Программирование GUI на Python. Курс]
• [Top Python Libraries Used In Data Science – Towards Data Science]
• [tqdm/tqdm: A Fast, Extensible Progress Bar for Python and CLI]
• [Travis CI]
• [Travis CI – Test and Deploy with Confidence]
• [Turn Python Scripts into Beautiful ML Tools – Towards Data Science]
• [Tutorial — ZODB documentation]
• [Tutorial: Web Scraping in R with rvest — Dataquest]
• [ucg8j/awesome-dash: A curated list of awesome Dash (plotly) resources]
• [umdjs/umd: UMD (Universal Module Definition) patterns for JavaScript modules that work everywhere.]
• [Understanding the GitHub flow · GitHub Guides]
• [Untitled Diagram – diagrams.net]
• [Untitled Document – Creately]
• [Usage · s0md3v/XSStrike Wiki]
• [User’s Guide — Matplotlib 3.2.1 documentation]
• [vaexio/vaex: Out-of-Core DataFrames for Python, ML, visualize and explore big tabular data at a billion rows per second 🚀]
• [vinta/awesome-python: A curated list of awesome Python frameworks, libraries, software and resources]
• [vstinner/pyperf: Toolkit to run Python benchmarks]
• [vue.js]
• [vue.min.js]
• [W3Schools Online Web Tutorials]
• [WAVE Report of Мої книги | KUPRIENKO]
• [Web technology for developers | MDN]
• [WebAssembly]
• [Website Style Guide Resources]
• [Welcome to pyjanitor’s documentation! — pyjanitor documentation]
• [Welcome to SymPy’s documentation! — SymPy 1.5.1 documentation]
• [What is Azure Machine Learning | Microsoft Docs]
• [Which movie should I watch? – Nishant Sahoo – Medium]
• [Wolfram Client Library for Python — Wolfram Client Library for Python 1.1.0 documentation]
• [Wolfram|Alpha: Computational Intelligence]
• [Yii PHP Framework]
• [Zsailer/pandas_flavor: The easy way to write your own flavor of Pandas]
• [Введение в потоки в Python – Еще один блог веб разработчика]
• [Введение в создание веб-приложений на Python]
• [Вступ · Django Girls Tutorial]
• [Глава 1: Создание первого приложения · Django в примерах]
• [Данные важнее, чем модели. Как выглядят эффективные процессы в Data Science | DOU]
• [Искусственная нейронная сеть с нуля на Python c библиотекой NumPy]
• [Как написать цепляющую вакансию на ДОУ | DOU]
• [Как оформить профиль на GitHub так, чтобы он работал при поиске работы | DOU]
• [Как получить 100/100 в Google Page Speed Test Tool? — SEO компания UAWEB]
• [Как установить Django, Nginx и Gunicorn на виртуальный сервер]
• [Краткое руководство по Dash — Python веб-фреймворк для создания дэшбордов. Installation + Dash Layout / Хабр]
• [Курс «Hacking PostgreSQL» : Компания Postgres Professional]
• [Лучшие датасеты для машинного обучения и анализа данных]
• [Мега-Учебник Flask, Часть 1: «Привет, Мир!» / Хабр]
• [Многопоточность в Python]
• [Многопоточность в Python. Библиотеки threading и multiprocessing.]
• [Многопоточность на примерах – модуль threading]
• [Настраиваем Django + virtualenv + nginx + gunicorn + PostgreSQL + memcached + letsencrypt на Ubuntu 16.04 | Python 3 для начинающих и чайников]
• [Настройка Django с Postgres, Nginx и Gunicorn в Ubuntu 18.04 | DigitalOcean]
• [Настройка веб-сервера для Django с nginx и uWSGI – Как стать программистом]
• [Обработка естественного языка: с чего начать и что изучать дальше]
• [Объектно-ориентированное Программирование в Python]
• [Описания паттернов проектирования. Паттерны проектирования. Шаблоны проектирования на Design pattern ru]
• [Перші кроки в NLP: розглядаємо Python-бібліотеку TensorFlow та нейронні мережі в реальному завданні | DOU]
• [Почему CSS Grid лучше, чем фреймворк Bootstrap?]
• [Почему Python хорош для Data Science и разработки приложений]
• [Предсказываем будущее с помощью библиотеки Facebook Prophet / Блог компании Open Data Science / Хабр]
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