❗ The course in English started on Feb. 5, 2018 as a series of articles (a "Publication" on Medium) with assignments and Kaggle Inclass competitions. Fill in this form to participate. ❗
These are the topics of Medium articles to appear from Feb 5 to Apr 7, 2018 (every Monday). The articles (Medium "stories" in a "Publication") are in English 🇬🇧. The Kaggle kernel "Vowpal Wabbit tutorial: blazingly fast learning" can serve as a demonstration of our materials. All articles in Russian are already published and are given here with 🇷🇺 icons (clickable). If you don't read Russian, still math, code and figures can give you an idea of what's going on. But all these articles are already translated into English and will be published on Medium from Feb 5 to Apr 7, 2018 📝
- Exploratory Data Analysis with Pandas 🇬🇧 🇷🇺
- Visual Data Analysis with Python 🇬🇧 🇷🇺
- Classification, Decision Trees and k Nearest Neighbors 🇬🇧 🇷🇺
- Linear Classification and Regression 🇬🇧 🇷🇺
- Bagging and Random Forest 🇬🇧 🇷🇺
- Feature Engineering and Feature Selection 🇷🇺
- Unsupervised Learning: Principal Component Analysis and Clustering 🇷🇺
- Vowpal Wabbit: Learning with Gigabytes of Data 🇬🇧 🇷🇺
- Time Series Analysis with Python 🇷🇺
- Gradient Boosting 🇷🇺
- "Exploratory data analysis with Pandas", nbviewer. Deadline: Feb. 11, 23.59 CET
- "Analyzing cardiovascular disease data", nbviewer. Deadline: Feb. 18, 23.59 CET
- "Decision trees with a toy task and the UCI Adult dataset", nbviewer. Deadline: Feb. 25, 23.59 CET
- "User Identification with Logistic Regression", nbviewer. Deadline: March 11, 23.59 CET
- "Logistic Regression and Random Forest in the Credit Scoring Problem", nbviewer. Deadline: March 18, 23.59 CET
- Catch Me If You Can: Intruder Detection through Webpage Session Tracking. Kaggle Inclass
- How good is your Medium article? Kaggle Inclass
Throughout the course we are maintaining a student rating. It takes into account credits scored in assignments and Kaggle competitions. Top-10 students (according to the final rating) will be list on a special Wiki page.
The discussions between students are held in the #eng_mlcourse_open channel of the OpenDataScience Slack team. Fill in this form to get an invitation. The form will also ask you some personal questions, don't hesitate 👋
- Prerequisites: Python, math and DevOps – how to get prepared for the course
- Software requirements and Docker container – this will guide you through installing all necessary stuff for working with course materials
- 1st session in English: all activities accounted for in rating
The course is free but you can support organizers by making a pledge on Patreon