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Open Machine Learning Course

ODS stickers

🇷🇺 Russian version 🇷🇺

❗ The next session launches on October 1, 2018. Fill in this form to participate. In September, you'll get an invitation to OpenDataScience Slack team ❗

Outline

This is the list of published articles on Medium 🇬🇧, Habr.com 🇷🇺, and jqr.com 🇨🇳. Icons 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 🇬🇧 🇷🇺 Kaggle Kernel
  9. Time Series Analysis with Python, part 1 🇬🇧 🇷🇺. Predicting future with Facebook Prophet, part 2 🇬🇧
  10. Gradient Boosting 🇬🇧 🇷🇺

Assignments

Each topic is followed by an assignment. Examples are to appear in the end of June, 2018.

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 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

The course is free but you can support organizers by making a pledge on Patreon

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OpenDataScience Machine Learning course. Both in English and Russian

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