festline/mlcourse_ai repository overview

⁠mlcourse.ai⁠, open Machine Learning course

For an up-to-date description, go to mlcourse.ai⁠

Current session launched on October 1, 2018. Fill in this form⁠ to participate, ou can still join

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

⁠Outline

This is the list of published articles on medium.com :uk:⁠, habr.com :ru:⁠, and jqr.com :cn:⁠. 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 :uk:⁠ :ru:⁠ :cn:⁠, Kaggle Kernel⁠
2. Visual Data Analysis with Python :uk:⁠ :ru:⁠ :cn:⁠, Kaggle Kernels: part1⁠, part2⁠
3. Classification, Decision Trees and k Nearest Neighbors :uk:⁠ :ru:⁠ :cn:⁠, Kaggle Kernel⁠
4. Linear Classification and Regression :uk:⁠ :ru:⁠ :cn:⁠, Kaggle Kernels: part1⁠, part2⁠, part3⁠, part4⁠, part5⁠
5. Bagging and Random Forest :uk:⁠ :ru:⁠ :cn:⁠, Kaggle Kernels: part1⁠, part2⁠, part3⁠
6. Feature Engineering and Feature Selection :uk:⁠ :ru:⁠ :cn:⁠, Kaggle Kernel⁠
7. Unsupervised Learning: Principal Component Analysis and Clustering :uk:⁠ :ru:⁠ :cn:⁠, Kaggle Kernel⁠
8. Vowpal Wabbit: Learning with Gigabytes of Data :uk:⁠ :ru:⁠ :cn:⁠, Kaggle Kernel⁠
9. Time Series Analysis with Python, part 1 :uk:⁠ :ru:⁠ :cn:⁠. Predicting future with Facebook Prophet, part 2 :uk:⁠, Kaggle Kernels: part1⁠, part2⁠
10. Gradient Boosting :uk:⁠ :ru:⁠, Kaggle Kernel⁠

⁠Lectures

Videolectures are uploaded to this⁠ YouTube playlist.

Introduction, video⁠, slides⁠

1. Exploratory data analysis with Pandas, video⁠. Discussion of the 1st demo assignment is here⁠

⁠Assignments

1. Exploratory Data Analysis of Olympic games with Pandas, nbviewer⁠. Deadline: October 14, 20:59 CET
2. Exploratory Data Analysis of US flights, nbviewer⁠. Deadline: October 21, 20:59 CET

These 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
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

⁠More info

Go to mlcourse.ai⁠

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