🇷🇺 Russian version 🇷🇺
❗ The next session is planned to start on Oct. 1, 2018. Fill in this form to participate:exclamation:
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 🇬🇧 🇷🇺
Each topic is followed by an assignment. Examples are to appear in May, 2018.
- 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 students (according to the final rating) will be listed on a special Wiki page.
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
