Open Machine Learning Course

🇷🇺 Russian version 🇷🇺

❗ 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 ❗

Kaggle Dataset.

Outline

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

Assignments

In a new run of the course, assignments will be announced each week. Meanwhile, you can pratice with demo versions. Solutions will be shared in the end of July, 2018.

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. Unupervised 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

The course is also available in a form of a Kaggle Dataset.

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 #eng_mlcourse_open 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 👋

Wiki Pages

• Prerequisites: Python, math and DevOps – how to get prepared for the course
• Software requirements and Docker container – this will guide you through installing all necessary stuff for working with course materials
• 1st session in English: all activities accounted for in rating

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