Mlcourse.AI

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mlcourse.ai – Open Machine Learning Course

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mlcourse.ai is an open Machine Learning course by OpenDataScience (ods.ai), led by Yury Kashnitsky (yorko), now Staff GenAI specialist at Google Cloud. Having both a Ph.D. degree in applied math and a Kaggle Competitions Master tier, Yury aimed at designing an ML course with a perfect balance between theory and practice. Thus, the course meets you with math formulae in lectures, and a lot of practice in the form of assignments and Kaggle Inclass competitions. Currently, the course is in a self-paced mode. Here, we guide you through the self-paced mlcourse.ai.

Bonus assignments

Additionally, you can purchase a Bonus Assignments pack with the best non-demo versions of mlcourse.ai assignments. Select the "Bonus Assignments" tier on Patreon or a similar tier on Boosty (rus).

<div class="row"> <div class="col-md-8" markdown="1"> <p align="center"> <a href="https://www.patreon.com/ods_mlcourse"> <img src="mlcourse_ai_jupyter_book/_static/img/become_a_patron.png"> </a> &nbsp;&nbsp; <a href="https://boosty.to/ods_mlcourse"> <img src="mlcourse_ai_jupyter_book/_static/img/boosty_logo.png" width=200px%> </a> </p> </div> <div class="col-md-4" markdown="1"> <details> <summary>Details of the deal</summary>

mlcourse.ai is still in self-paced mode but we offer you Bonus Assignments with solutions for a contribution of $17/month. The idea is that you pay for ~1-5 months while studying the course materials, but a single contribution is still fine and opens your access to the bonus pack.

Note: the first payment is charged at the moment of joining the Tier Patreon, and the next payment is charged on the 1st day of the next month, thus it's better to purchase the pack in the 1st half of the month.

mlcourse.ai is never supposed to go fully monetized (it's created in the wonderful open ODS.ai community and will remain open and free) but it'd help to cover some operational costs, and Yury also put in quite some effort into assembling all the best assignments into one pack. Please note that unlike the rest of the course content, Bonus Assignments are copyrighted. Informally, Yury's fine if you share the pack with 2-3 friends but public sharing of the Bonus Assignments pack is prohibited.

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The bonus pack contains 10 assignments, in some of them you are challenged to beat a baseline in a Kaggle competition under thorough guidance ("Alice" and "Medium") or implement an algorithm from scratch -- efficient stochastic gradient descent classifier and gradient boosting.

Self-paced passing

You are guided through 10 weeks of mlcourse.ai. For each week, from Pandas to Gradient Boosting, instructions are given on which articles to read, lectures to watch, and what assignments to accomplish.

Articles

This is the list of published articles on medium.com :uk:, habr.com :ru:. Notebooks in Chinese :cn: are also mentioned, and links to Kaggle Notebooks (in English) are provided. Icons are clickable.

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

Lectures

Video lectures are uploaded to this YouTube playlist. Introduction, [video](https://www.youtub