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Open Machine Learning Course

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πŸ‡·πŸ‡Ί Russian version πŸ‡·πŸ‡Ί

❗ The next session is planned to launch on February 11, 2019. Fill in this form to participate. Wait for more details in the end of January ❗

Mirrors (πŸ‡¬πŸ‡§-only): (main site), Kaggle Dataset (same notebooks as Kernels)


This is the list of published articles on πŸ‡¬πŸ‡§, πŸ‡·πŸ‡Ί, and πŸ‡¨πŸ‡³. Icons are clickable. Also, links to Kaggle Kernels (in English) are given. This way one can reproduce everything without installing a single package.

  1. Exploratory Data Analysis with Pandas πŸ‡¬πŸ‡§ πŸ‡·πŸ‡Ί πŸ‡¨πŸ‡³, Kaggle Kernel
  2. Visual Data Analysis with Python πŸ‡¬πŸ‡§ πŸ‡·πŸ‡Ί πŸ‡¨πŸ‡³, Kaggle Kernels: part1, part2
  3. Classification, Decision Trees and k Nearest Neighbors πŸ‡¬πŸ‡§ πŸ‡·πŸ‡Ί πŸ‡¨πŸ‡³, Kaggle Kernel
  4. Linear Classification and Regression πŸ‡¬πŸ‡§ πŸ‡·πŸ‡Ί πŸ‡¨πŸ‡³, Kaggle Kernels: part1, part2, part3, part4, part5
  5. Bagging and Random Forest πŸ‡¬πŸ‡§ πŸ‡·πŸ‡Ί πŸ‡¨πŸ‡³, Kaggle Kernels: part1, part2, part3
  6. Feature Engineering and Feature Selection πŸ‡¬πŸ‡§ πŸ‡·πŸ‡Ί πŸ‡¨πŸ‡³, Kaggle Kernel
  7. Unsupervised Learning: Principal Component Analysis and Clustering πŸ‡¬πŸ‡§ πŸ‡·πŸ‡Ί πŸ‡¨πŸ‡³, Kaggle Kernel
  8. Vowpal Wabbit: Learning with Gigabytes of Data πŸ‡¬πŸ‡§ πŸ‡·πŸ‡Ί πŸ‡¨πŸ‡³, Kaggle Kernel
  9. Time Series Analysis with Python, part 1 πŸ‡¬πŸ‡§ πŸ‡·πŸ‡Ί πŸ‡¨πŸ‡³. Predicting future with Facebook Prophet, part 2 πŸ‡¬πŸ‡§, πŸ‡¨πŸ‡³ Kaggle Kernels: part1, part2
  10. Gradient Boosting πŸ‡¬πŸ‡§ πŸ‡·πŸ‡Ί, πŸ‡¨πŸ‡³, Kaggle Kernel


Videolectures are uploaded to this YouTube playlist. Introduction, video, slides

  1. Exploratory data analysis with Pandas, video
  2. Visualization, main plots for EDA, video
  3. Decision trees: theory and practical part
  4. Logistic regression: theoretical foundations, practical part (baselines in the "Alice" competition)
  5. Emsembles and Random Forest – part 1. Classification metrics – part 2. Example of a business task, predicting a customer payment – part 3
  6. Linear regression and regularization - theory, LASSO & Ridge, LTV prediction - practice
  7. Unsupervised learning - Principal Component Analysis and Clustering
  8. Stochastic Gradient Descent for classification and regression - part 1, part 2 TBA
  9. Time series analysis with Python (ARIMA, Prophet) - video
  10. Gradient boosting: basic ideas - part 1, key ideas behind Xgboost, LightGBM, and CatBoost + practice - part 2


The following are demo versions. Full versions are announced during course sessions.

  1. Exploratory data analysis with Pandas, nbviewer, Kaggle Kernel
  2. Analyzing cardiovascular disease data, nbviewer, Kaggle Kernel
  3. Decision trees with a toy task and the UCI Adult dataset, nbviewer, Kaggle Kernel
  4. Linear Regression as an optimization problem, nbviewer, Kaggle Kernel
  5. Logistic Regression and Random Forest in the credit scoring problem, nbviewer, Kaggle Kernel
  6. Exploring OLS, Lasso and Random Forest in a regression task, nbviewer, Kaggle Kernel
  7. Unsupervised learning, nbviewer, Kaggle Kernel
  8. Implementing online regressor, nbviewer, Kaggle Kernel
  9. Time series analysis, nbviewer, Kaggle Kernel
  10. Beating baseline in a competition, Kaggle kernel

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


Throughout the course we are maintaining a student rating. It takes into account credits scored in assignments and Kaggle competitions. They say, rating highly motivates to finish the course. Top students (according to the final rating) are listed on a special page.


Discussions between students are held in the #mlcourse_ai channel of the OpenDataScience Slack team. Fill in this form to get an invitation (prior to February 11, 2019). The form will also ask you some personal questions, don't hesitate πŸ‘‹

The course is free but you can support organizers by making a pledge on Patreon (monthly support) or a one-time payment on Ko-fi. Thus you'll foster the spread of Machine Learning in the world!


Open Machine Learning Course



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