Open Machine Learning Course
🇷🇺 Russian version 🇷🇺
❗ The course in English started on Feb. 5, 2018 as a series of articles (a "Publication" on Medium) with assignments and Kaggle Inclass competitions. The next session is planned to start on Oct. 1, 2018. Fill in this form to participate❗
Outline
This is the list of published articles on Medium 🇬🇧, Habrahabr 🇷🇺, and jqr.com 🇨🇳. Icons are clickable.
- 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 🇬🇧 🇷🇺 Kaggle Kernel
- Time Series Analysis with Python, part 1 🇬🇧 🇷🇺. Predicting future with Facebook Prophet, part 2 🇬🇧
- Gradient Boosting 🇬🇧 🇷🇺
Assignments
- "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
- Beating benchmarks in two Kaggle Inclass competitons. Part 1, "Alice", nbviewer. Part 2, "Medium", nbviewer. Deadline: April 1, 23.59 CET
- Unsupervised learning: PCA and clustering, nbviewer. Deadline: April 4, 23.59 CET
- Vowpal Wabbit and StackOverflow questions, nbviewer. Deadline: April 15, 23.59 CET
- Time series analysis, nbviewer. Deadline: April 15, 23.59 CET
- Gradient boosting and flight delays, nbviewer. Deadline: April 22, 23.59 CET
Kaggle competitions
- Catch Me If You Can: Intruder Detection through Webpage Session Tracking. Kaggle Inclass
- How good is your Medium article? Kaggle Inclass
Rating
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 listed on a special Wiki page.
Community
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 👋
Wiki Pages
- 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