Open Machine Learning course mlcourse.ai, both in English and Russian
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README.md

mlcourse.ai, open Machine Learning course

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πŸ‡·πŸ‡Ί Russian version πŸ‡·πŸ‡Ί

❗️ 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 ❗️

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

Outline

This is the list of published articles on medium.com πŸ‡¬πŸ‡§, habr.com πŸ‡·πŸ‡Ί, and jqr.com 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‡§, Kaggle Kernels: part1, part2
  10. Gradient Boosting πŸ‡¬πŸ‡§ πŸ‡·πŸ‡Ί, Kaggle Kernel

Assignments

Full assignments are announced each week in a new run of the course (October 1, 2018). Meanwhile, you can pratice with demo versions. Solutions to both demo and full versions will be discussed in the upcoming run of the course.

  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. Gradient boosting and flight delays, nbviewer, 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

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 #mlcourse_ai 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 πŸ‘‹

Wiki Pages

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