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🇷🇺 Russian version 🇷🇺

❗ The next session is planned to launch on February 11, 2019. Please wait for an announcement in January ❗

Mirrors (:uk:-only): mlcourse.ai (main site), Kaggle Dataset (same notebooks as Kernels)

This is the list of published articles on medium.com 🇬🇧, habr.com 🇷🇺, and jqr.com 🇨🇳. 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 🇬🇧 🇷🇺 🇨🇳, Kaggle Kernel
2. Visual Data Analysis with Python 🇬🇧 🇷🇺 🇨🇳, Kaggle Kernels: part1, part2
3. Classification, Decision Trees and k Nearest Neighbors 🇬🇧 🇷🇺 🇨🇳, Kaggle Kernel
4. Linear Classification and Regression 🇬🇧 🇷🇺 🇨🇳, Kaggle Kernels: part1, part2, part3, part4, part5
5. Bagging and Random Forest 🇬🇧 🇷🇺 🇨🇳, Kaggle Kernels: part1, part2, part3
6. Feature Engineering and Feature Selection 🇬🇧 🇷🇺 🇨🇳, Kaggle Kernel
7. Unsupervised Learning: Principal Component Analysis and Clustering 🇬🇧 🇷🇺 🇨🇳, Kaggle Kernel
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 🇬🇧, 🇨🇳 Kaggle Kernels: part1, part2
10. Gradient Boosting 🇬🇧 🇷🇺, 🇨🇳, 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

1. Exploratory Data Analysis of Olympic games with Pandas. nbviewer. Deadline: October 14, 21:59 UTC+2
2. Exploratory Data Analysis of US flights. nbviewer. Deadline: October 21, 21:59 UTC+2
3. Decision trees. nbviewer. Deadline: October 28, 21:59 UTC+2. Optional: implementing a decision tree algorithm, nvbiewer (no webforms and credits, the same deadline)
4. Logisitic regression. nbviewer. Deadline: November 4, 21:59 UTC+2
5. Random Forest and Logistic Regression in credit scoring and movie reviews classification. nbviewer. Deadline: November 11, 21:59 UTC+2
6. Beating baselines in "How good is your Medium article?". nbviewer. Deadline: November 18, 21:59 UTC+2
7. PCA and clustering. nbviewer. Deadline: November 25, 21:59 UTC+2
8. StackOverflow questions tagging with logistic regression. nbviewer. Deadline: December 2, 21:59 UTC+2
9. Time series analysis. nbviewer. Deadline: December 16, 21:59 UTC+2
10. Flight delays prediction, beating simple baselines. Kaggle Kernel. Deadline: December 16, 21:59 UTC+2

The following are demo versions. Just for practice, they don't have an impact on rating.

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

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. 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 👋

Go to mlcourse.ai

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