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mlcourse.ai – Open Machine Learning Course

License: CC BY-NC-SA 4.0 Slack Donate Donate

mlcourse.ai is an open Machine Learning course by OpenDataScience. The course is designed to perfectly balance theory and practice. You can take part in several Kaggle Inclass competitions held during the course. From spring 2017 to fall 2019, 6 sessions of mlcourse.ai took place - 26k participants applied, 10k converted to passing the first assignment, about 1500 participants finished the course. Currently, the course is in self-paced mode. A thorough roadmap guiding you through the self-paced mlcourse.ai will be published in February, 2020.

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

Outline

This is the list of published articles on medium.com 🇬🇧, habr.com 🇷🇺. Also notebooks in Chinese are mentioned 🇨🇳 and links to Kaggle Kernels (in English) are given. Icons are clickable.

  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
  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. Ensembles 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

Demo assignments

  1. Exploratory data analysis with Pandas, nbviewer, Kaggle Kernel, solution
  2. Analyzing cardiovascular disease data, nbviewer, Kaggle Kernel, solution
  3. Decision trees with a toy task and the UCI Adult dataset, nbviewer, Kaggle Kernel, solution
  4. Sarcasm detection, Kaggle Kernel, solution. Linear Regression as an optimization problem, nbviewer, Kaggle Kernel
  5. Logistic Regression and Random Forest in the credit scoring problem, nbviewer, Kaggle Kernel, solution
  6. Exploring OLS, Lasso and Random Forest in a regression task, nbviewer, Kaggle Kernel, solution
  7. Unsupervised learning, nbviewer, Kaggle Kernel, solution
  8. Implementing online regressor, nbviewer, Kaggle Kernel, solution
  9. Time series analysis, nbviewer, Kaggle Kernel, solution
  10. Beating baseline in a competition, Kaggle Kernel

Kaggle competitions

  1. Catch Me If You Can: Intruder Detection through Webpage Session Tracking. Kaggle Inclass
  2. DotA 2 winner prediction Kaggle Inclass

Community

Discussions are held in the #mlcourse_ai channel of the OpenDataScience (ods.ai) Slack team.

The course is free but you can support organizers by making a pledge on Patreon (monthly support) or a one-time payment on Ko-fi. Thus you'll foster the spread of Machine Learning in the world!

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