mlcourse.ai, 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 ❗

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

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

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

Demo assignments, just for practice, not to be accounted in 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 👋

Wiki Pages

• Prerequisites: Python, math, software, and DevOps – how to get prepared for the course
• 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