Open Machine Learning Course
β 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 participateβ
Outline
Icons π¬π§ and π·πΊ are clickable.
- Exploratory Data Analysis with Pandas π¬π§ π·πΊ
- Visual Data Analysis with Python π¬π§ π·πΊ
- Classification, Decision Trees and k Nearest Neighbors π¬π§ π·πΊ
- Linear Classification and Regression π¬π§ π·πΊ
- Bagging and Random Forest π¬π§ π·πΊ
- Feature Engineering and Feature Selection π·πΊ
- Unsupervised Learning: Principal Component Analysis and Clustering π·πΊ
- Vowpal Wabbit: Learning with Gigabytes of Data π¬π§ π·πΊ
- Time Series Analysis with Python π·πΊ
- Gradient Boosting π·πΊ
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: March 25, 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 π
Wiki Pages
- Prerequisites: Python, math and DevOps β how to get prepared for the course
- Software requirements and Docker container β 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