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:exclamation:

Outline

Icons 🇬🇧 🇷🇺 and 🇨🇳 are clickable.

1. Exploratory Data Analysis with Pandas 🇬🇧 🇷🇺 🇨🇳
2. Visual Data Analysis with Python 🇬🇧 🇷🇺 🇨🇳
3. Classification, Decision Trees and k Nearest Neighbors 🇬🇧 🇷🇺
4. Linear Classification and Regression 🇬🇧 🇷🇺
5. Bagging and Random Forest 🇬🇧 🇷🇺
6. Feature Engineering and Feature Selection 🇬🇧 🇷🇺
7. Unsupervised Learning: Principal Component Analysis and Clustering 🇷🇺
8. Vowpal Wabbit: Learning with Gigabytes of Data 🇬🇧 🇷🇺
9. Time Series Analysis with Python 🇷🇺
10. Gradient Boosting 🇷🇺

Assignments

1. "Exploratory data analysis with Pandas", nbviewer. Deadline: Feb. 11, 23.59 CET
2. "Analyzing cardiovascular disease data", nbviewer. Deadline: Feb. 18, 23.59 CET
3. "Decision trees with a toy task and the UCI Adult dataset", nbviewer. Deadline: Feb. 25, 23.59 CET
4. "User Identification with Logistic Regression", nbviewer. Deadline: March 11, 23.59 CET
5. "Logistic Regression and Random Forest in the Credit Scoring Problem", nbviewer. Deadline: March 18, 23.59 CET
6. Beating benchmarks in two Kaggle Inclass competitons. Part 1, "Alice", nbviewer. Part 2, "Medium", nbviewer. Deadline: April 1, 23.59 CET

Kaggle competitions

1. Catch Me If You Can: Intruder Detection through Webpage Session Tracking. Kaggle Inclass
2. 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