manura-d/mlcourse_open

OpenDataScience Machine Learning course. Both in English and Russian

Open Machine Learning Course

🇷🇺 Russian version 🇷🇺

❗ The next session is planned to start on Oct. 1, 2018. Fill in this form to participate:exclamation:

Outline

This is the list of published articles on Medium 🇬🇧, Habr.com 🇷🇺, and jqr.com 🇨🇳. Icons are clickable.

1. Exploratory Data Analysis with Pandas 🇬🇧 🇷🇺 🇨🇳
2. Visual Data Analysis with Python 🇬🇧 🇷🇺 🇨🇳
3. Classification, Decision Trees and k Nearest Neighbors 🇬🇧 🇷🇺 🇨🇳
4. Linear Classification and Regression 🇬🇧 🇷🇺 🇨🇳
5. Bagging and Random Forest 🇬🇧 🇷🇺
6. Feature Engineering and Feature Selection 🇬🇧 🇷🇺
7. Unsupervised Learning: Principal Component Analysis and Clustering 🇬🇧 🇷🇺
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 🇬🇧
10. Gradient Boosting 🇬🇧 🇷🇺

Assignments

Each topic is followed by an assignment. Examples are to appear in the end of June, 2018.

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 #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