mlcourse.ai, open Machine Learning course
๐ท๐บ Russian version ๐ท๐บ
โ Current session launched on October 1, 2018. Fill in this form to participate, you can still join โ
Mirrors (:uk:-only): mlcourse.ai (main site), Kaggle Dataset (same notebooks as Kernels)
This is the list of published articles on medium.com ๐ฌ๐ง, habr.com ๐ท๐บ, and jqr.com ๐จ๐ณ. Icons are clickable. Also, links to Kaggle Kernels (in English) are given. This way one can reproduce everything without installing a single package.
- Exploratory Data Analysis with Pandas ๐ฌ๐ง ๐ท๐บ ๐จ๐ณ, Kaggle Kernel
- Visual Data Analysis with Python ๐ฌ๐ง ๐ท๐บ ๐จ๐ณ, Kaggle Kernels: part1, part2
- Classification, Decision Trees and k Nearest Neighbors ๐ฌ๐ง ๐ท๐บ ๐จ๐ณ, Kaggle Kernel
- Linear Classification and Regression ๐ฌ๐ง ๐ท๐บ ๐จ๐ณ, Kaggle Kernels: part1, part2, part3, part4, part5
- Bagging and Random Forest ๐ฌ๐ง ๐ท๐บ ๐จ๐ณ, Kaggle Kernels: part1, part2, part3
- Feature Engineering and Feature Selection ๐ฌ๐ง ๐ท๐บ ๐จ๐ณ, Kaggle Kernel
- Unsupervised Learning: Principal Component Analysis and Clustering ๐ฌ๐ง ๐ท๐บ ๐จ๐ณ, Kaggle Kernel
- Vowpal Wabbit: Learning with Gigabytes of Data ๐ฌ๐ง ๐ท๐บ ๐จ๐ณ, Kaggle Kernel
- Time Series Analysis with Python, part 1 ๐ฌ๐ง ๐ท๐บ ๐จ๐ณ. Predicting future with Facebook Prophet, part 2 ๐ฌ๐ง, Kaggle Kernels: part1, part2
- Gradient Boosting ๐ฌ๐ง ๐ท๐บ, Kaggle Kernel
Videolectures are uploaded to this YouTube playlist.
- Exploratory data analysis with Pandas, video
- Visualization, main plots for EDA, video
- Decision trees: theory and practical part
- Logistic regression: theoretical foundations, practical part (baselines in the "Alice" competition)
- Exploratory Data Analysis of Olympic games with Pandas, nbviewer. Deadline: October 14, 21:59 UTC+2
- Exploratory Data Analysis of US flights, nbviewer. Deadline: October 21, 21:59 UTC+2
- Decision trees. nbviewer. Deadline: October 28, 21:59 UTC+2. Optional: implementing a decision tree algorithm, nvbiewer (no webforms and credits, the same deadline)
- Logisitic regression. nbviewer. Deadline: November 4, 21:59 UTC+2
These are demo versions. Just for practice, they don't have an impact on rating.
- Exploratory data analysis with Pandas, nbviewer, Kaggle Kernel
- Analyzing cardiovascular disease data, nbviewer, Kaggle Kernel
- Decision trees with a toy task and the UCI Adult dataset, nbviewer, Kaggle Kernel
- Linear Regression as an optimization problem, nbviewer, Kaggle Kernel
- Logistic Regression and Random Forest in the credit scoring problem, nbviewer, Kaggle Kernel
- Exploring OLS, Lasso and Random Forest in a regression task, nbviewer, Kaggle Kernel
- Unsupervised learning, nbviewer, Kaggle Kernel
- Implementing online regressor, nbviewer, Kaggle Kernel
- Time series analysis, nbviewer, Kaggle Kernel
- Gradient boosting and flight delays, nbviewer, Kaggle Kernel
- 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 #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 ๐
Go to mlcourse.ai
The course is free but you can support organizers by making a pledge on Patreon