[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"project-9664":3},{"id":4,"name":5,"fullName":6,"owner":7,"repo":5,"description":8,"homepage":9,"htmlUrl":10,"language":11,"languages":10,"totalLinesOfCode":10,"stars":12,"forks":13,"watchers":14,"openIssues":15,"contributorsCount":16,"subscribersCount":16,"size":16,"stars1d":17,"stars7d":18,"stars30d":19,"stars90d":16,"forks30d":16,"starsTrendScore":20,"compositeScore":21,"rankGlobal":10,"rankLanguage":10,"license":22,"archived":23,"fork":23,"defaultBranch":24,"hasWiki":25,"hasPages":25,"topics":26,"createdAt":10,"pushedAt":10,"updatedAt":44,"readmeContent":45,"aiSummary":46,"trendingCount":16,"starSnapshotCount":16,"syncStatus":15,"lastSyncTime":47,"discoverSource":48},9664,"mlcourse.ai","Yorko\u002Fmlcourse.ai","Yorko","Open Machine Learning Course","https:\u002F\u002Fmlcourse.ai",null,"Python",10625,5713,567,2,0,1,18,45,6,85.5,"Other",false,"main",true,[27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43],"algorithms","data-analysis","data-science","docker","ipynb","kaggle-inclass","machine-learning","math","matplotlib","numpy","pandas","plotly","python","scikit-learn","scipy","seaborn","vowpal-wabbit","2026-06-12 04:00:46","\u003Cdiv align=\"center\">\n\n![ODS stickers](https:\u002F\u002Fgithub.com\u002FYorko\u002Fmlcourse.ai\u002Fblob\u002Fmain\u002Fimg\u002Fods_stickers.jpg)\n\n**[mlcourse.ai](https:\u002F\u002Fmlcourse.ai) – Open Machine Learning Course**\n\n[![License: CC BY-NC-SA 4.0](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002Flicense-CC%20BY--NC--SA%204.0-green)](https:\u002F\u002Fcreativecommons.org\u002Flicenses\u002Fby-nc-sa\u002F4.0\u002F)\n[![Donate](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002Fsupport-patreon-red)](https:\u002F\u002Fwww.patreon.com\u002Fods_mlcourse)\n[![Donate](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002Fsupport-ko--fi-red)](https:\u002F\u002Fko-fi.com\u002Fmlcourse_ai)\n\n\u003C\u002Fdiv>\n\n[mlcourse.ai](https:\u002F\u002Fmlcourse.ai) is an open Machine Learning course by [OpenDataScience (ods.ai)](https:\u002F\u002Fods.ai\u002F), led by [Yury Kashnitsky (yorko)](https:\u002F\u002Fyorko.github.io\u002F), now Staff GenAI specialist at Google Cloud. Having both a Ph.D. degree in applied math and a Kaggle Competitions Master tier, Yury aimed at designing an ML course with a perfect balance between theory and practice. Thus, the course meets you with math formulae in lectures, and a lot of practice in the form of assignments and Kaggle Inclass competitions. Currently, the course is in a **self-paced mode**. Here, we guide you through the self-paced [mlcourse.ai](https:\u002F\u002Fmlcourse.ai).\n\n### Bonus assignments\n\nAdditionally, you can purchase a **Bonus Assignments pack** with the best non-demo versions of [mlcourse.ai](https:\u002F\u002Fmlcourse.ai\u002F) assignments. Select the [\"Bonus Assignments\" tier](https:\u002F\u002Fwww.patreon.com\u002Fods_mlcourse) on Patreon or a [similar tier](https:\u002F\u002Fboosty.to\u002Fods_mlcourse\u002Fpurchase\u002F1142055?ssource=DIRECT&share=subscription_link) on Boosty (rus).\n\n\u003Cdiv class=\"row\">\n  \u003Cdiv class=\"col-md-8\" markdown=\"1\">\n  \u003Cp align=\"center\">\n  \u003Ca href=\"https:\u002F\u002Fwww.patreon.com\u002Fods_mlcourse\">\n         \u003Cimg src=\"mlcourse_ai_jupyter_book\u002F_static\u002Fimg\u002Fbecome_a_patron.png\">\n  \u003C\u002Fa>\n    \n  \u003Ca href=\"https:\u002F\u002Fboosty.to\u002Fods_mlcourse\">\n         \u003Cimg src=\"mlcourse_ai_jupyter_book\u002F_static\u002Fimg\u002Fboosty_logo.png\" width=200px%>\n  \u003C\u002Fa>\n  \u003C\u002Fp>\n\n\u003C\u002Fdiv>\n\n  \u003Cdiv class=\"col-md-4\" markdown=\"1\">\n  \u003Cdetails>\n  \u003Csummary>Details of the deal\u003C\u002Fsummary>\n\nmlcourse.ai is still in self-paced mode but we offer you Bonus Assignments with solutions for a contribution of $17\u002Fmonth. The idea is that you pay for ~1-5 months while studying the course materials, but a single contribution is still fine and opens your access to the bonus pack.\n\nNote: the first payment is charged at the moment of joining the Tier Patreon, and the next payment is charged on the 1st day of the next month, thus it's better to purchase the pack in the 1st half of the month.\n\nmlcourse.ai is never supposed to go fully monetized (it's created in the wonderful open ODS.ai community and will remain open and free) but it'd help to cover some operational costs, and Yury also put in quite some effort into assembling all the best assignments into one pack. Please note that unlike the rest of the course content, Bonus Assignments are copyrighted. Informally, Yury's fine if you share the pack with 2-3 friends but public sharing of the Bonus Assignments pack is prohibited.\n\u003C\u002Fdetails>\n  \u003C\u002Fdiv>\n\u003C\u002Fdiv>\u003Cbr>\n\nThe bonus pack contains 10 assignments, in some of them you are challenged to beat a baseline in a Kaggle competition under thorough guidance ([\"Alice\"](https:\u002F\u002Fmlcourse.ai\u002Fbook\u002Ftopic04\u002Fbonus_assignment04_alice_baselines.html) and [\"Medium\"](https:\u002F\u002Fmlcourse.ai\u002Fbook\u002Ftopic06\u002Fbonus_assignment06.html)) or implement an algorithm from scratch -- efficient stochastic gradient descent [classifier](https:\u002F\u002Fmlcourse.ai\u002Fbook\u002Ftopic08\u002Fbonus_assignment08.html) and [gradient boosting](https:\u002F\u002Fmlcourse.ai\u002Fbook\u002Ftopic10\u002Fbonus_assignment10.html).\n\n### Self-paced passing\nYou are guided through 10 weeks of [mlcourse.ai](https:\u002F\u002Fmlcourse.ai). For each week, from Pandas to Gradient Boosting, instructions are given on which articles to read, lectures to watch, and what assignments to accomplish.\n\n### Articles\nThis is the list of published articles on medium.com [:uk:](https:\u002F\u002Fmedium.com\u002Fopen-machine-learning-course), habr.com [:ru:](https:\u002F\u002Fhabr.com\u002Fcompany\u002Fods\u002Fblog\u002F344044\u002F). Notebooks in Chinese :cn: are also mentioned, and links to Kaggle Notebooks (in English) are provided. Icons are clickable.\n\n1. Exploratory Data Analysis with Pandas [:uk:](https:\u002F\u002Fmlcourse.ai\u002Fbook\u002Ftopic01\u002Ftopic01_pandas_data_analysis.html)  [:ru:](https:\u002F\u002Fhabrahabr.ru\u002Fcompany\u002Fods\u002Fblog\u002F322626\u002F) [:cn:](https:\u002F\u002Fnbviewer.jupyter.org\u002Fgithub\u002FYorko\u002Fmlcourse.ai\u002Fblob\u002Fmain\u002Fjupyter_chinese\u002Ftopic01-%E4%BD%BF%E7%94%A8-Pandas-%E8%BF%9B%E8%A1%8C%E6%95%B0%E6%8D%AE%E6%8E%A2%E7%B4%A2.ipynb), [Kaggle Notebook](https:\u002F\u002Fwww.kaggle.com\u002Fkashnitsky\u002Ftopic-1-exploratory-data-analysis-with-pandas)\n2. Visual Data Analysis with Python [:uk:](https:\u002F\u002Fmlcourse.ai\u002Fbook\u002Ftopic02\u002Ftopic02_visual_data_analysis.html)  [:ru:](https:\u002F\u002Fhabrahabr.ru\u002Fcompany\u002Fods\u002Fblog\u002F323210\u002F) [:cn:](http:\u002F\u002Fnbviewer.ipython.org\u002Furls\u002Fraw.github.com\u002FYorko\u002Fmlcourse.ai\u002Fmain\u002Fjupyter_chinese\u002Ftopic02-Python-%E6%95%B0%E6%8D%AE%E5%8F%AF%E8%A7%86%E5%8C%96%E5%88%86%E6%9E%90.ipynb), Kaggle Notebooks: [part1](https:\u002F\u002Fwww.kaggle.com\u002Fkashnitsky\u002Ftopic-2-visual-data-analysis-in-python), [part2](https:\u002F\u002Fwww.kaggle.com\u002Fkashnitsky\u002Ftopic-2-part-2-seaborn-and-plotly)\n3. Classification, Decision Trees and k Nearest Neighbors [:uk:](https:\u002F\u002Fmlcourse.ai\u002Fbook\u002Ftopic03\u002Ftopic03_decision_trees_kNN.html) [:ru:](https:\u002F\u002Fhabrahabr.ru\u002Fcompany\u002Fods\u002Fblog\u002F322534\u002F) [:cn:](https:\u002F\u002Fnbviewer.jupyter.org\u002Fgithub\u002FYorko\u002Fmlcourse.ai\u002Fblob\u002Fmain\u002Fjupyter_chinese\u002Ftopic03-%E5%86%B3%E7%AD%96%E6%A0%91%E5%92%8C-K-%E8%BF%91%E9%82%BB%E5%88%86%E7%B1%BB.ipynb), [Kaggle Notebook](https:\u002F\u002Fwww.kaggle.com\u002Fkashnitsky\u002Ftopic-3-decision-trees-and-knn)\n4. Linear Classification and Regression [:uk:](https:\u002F\u002Fmlcourse.ai\u002Fbook\u002Ftopic04\u002Ftopic4_linear_models_part1_mse_likelihood_bias_variance.html) [:ru:](https:\u002F\u002Fhabrahabr.ru\u002Fcompany\u002Fods\u002Fblog\u002F323890\u002F) [:cn:](http:\u002F\u002Fnbviewer.ipython.org\u002Furls\u002Fraw.github.com\u002FYorko\u002Fmlcourse.ai\u002Fmain\u002Fjupyter_chinese\u002Ftopic04-%E7%BA%BF%E6%80%A7%E5%9B%9E%E5%BD%92%E5%92%8C%E7%BA%BF%E6%80%A7%E5%88%86%E7%B1%BB%E5%99%A8.ipynb), Kaggle Notebooks: [part1](https:\u002F\u002Fwww.kaggle.com\u002Fkashnitsky\u002Ftopic-4-linear-models-part-1-ols), [part2](https:\u002F\u002Fwww.kaggle.com\u002Fkashnitsky\u002Ftopic-4-linear-models-part-2-classification), [part3](https:\u002F\u002Fwww.kaggle.com\u002Fkashnitsky\u002Ftopic-4-linear-models-part-3-regularization), [part4](https:\u002F\u002Fwww.kaggle.com\u002Fkashnitsky\u002Ftopic-4-linear-models-part-4-more-of-logit), [part5](https:\u002F\u002Fwww.kaggle.com\u002Fkashnitsky\u002Ftopic-4-linear-models-part-5-validation)\n5. Bagging and Random Forest [:uk:](https:\u002F\u002Fmlcourse.ai\u002Fbook\u002Ftopic05\u002Ftopic5_part1_bagging.html) [:ru:](https:\u002F\u002Fhabrahabr.ru\u002Fcompany\u002Fods\u002Fblog\u002F324402\u002F) [:cn:](https:\u002F\u002Fnbviewer.jupyter.org\u002Fgithub\u002FYorko\u002Fmlcourse.ai\u002Fblob\u002Fmain\u002Fjupyter_chinese\u002Ftopic05-%E9%9B%86%E6%88%90%E5%AD%A6%E4%B9%A0%E5%92%8C%E9%9A%8F%E6%9C%BA%E6%A3%AE%E6%9E%97%E6%96%B9%E6%B3%95.ipynb), Kaggle Notebooks: [part1](https:\u002F\u002Fwww.kaggle.com\u002Fkashnitsky\u002Ftopic-5-ensembles-part-1-bagging), [part2](https:\u002F\u002Fwww.kaggle.com\u002Fkashnitsky\u002Ftopic-5-ensembles-part-2-random-forest), [part3](https:\u002F\u002Fwww.kaggle.com\u002Fkashnitsky\u002Ftopic-5-ensembles-part-3-feature-importance)\n6. Feature Engineering and Feature Selection [:uk:](https:\u002F\u002Fmlcourse.ai\u002Fbook\u002Ftopic06\u002Ftopic6_feature_engineering_feature_selection.html) [:ru:](https:\u002F\u002Fhabrahabr.ru\u002Fcompany\u002Fods\u002Fblog\u002F325422\u002F) [:cn:](http:\u002F\u002Fnbviewer.ipython.org\u002Furls\u002Fraw.github.com\u002FYorko\u002Fmlcourse.ai\u002Fmain\u002Fjupyter_chinese\u002Ftopic06-%E7%89%B9%E5%BE%81%E5%B7%A5%E7%A8%8B%E5%92%8C%E7%89%B9%E5%BE%81%E9%80%89%E6%8B%A9.ipynb), [Kaggle Notebook](https:\u002F\u002Fwww.kaggle.com\u002Fkashnitsky\u002Ftopic-6-feature-engineering-and-feature-selection)\n7. Unsupervised Learning: Principal Component Analysis and Clustering [:uk:](https:\u002F\u002Fmlcourse.ai\u002Fbook\u002Ftopic07\u002Ftopic7_pca_clustering.html) [:ru:](https:\u002F\u002Fhabrahabr.ru\u002Fcompany\u002Fods\u002Fblog\u002F325654\u002F) [:cn:](http:\u002F\u002Fnbviewer.ipython.org\u002Furls\u002Fraw.github.com\u002FYorko\u002Fmlcourse.ai\u002Fmain\u002Fjupyter_chinese\u002Ftopic07-%E4%B8%BB%E6%88%90%E5%88%86%E5%88%86%E6%9E%90%E5%92%8C%E8%81%9A%E7%B1%BB.ipynb), [Kaggle Notebook](https:\u002F\u002Fwww.kaggle.com\u002Fkashnitsky\u002Ftopic-7-unsupervised-learning-pca-and-clustering)\n8. Vowpal Wabbit: Learning with Gigabytes of Data [:uk:](https:\u002F\u002Fmlcourse.ai\u002Fbook\u002Ftopic08\u002Ftopic08_sgd_hashing_vowpal_wabbit.html) [:ru:](https:\u002F\u002Fhabrahabr.ru\u002Fcompany\u002Fods\u002Fblog\u002F326418\u002F) [:cn:](https:\u002F\u002Fnbviewer.jupyter.org\u002Fgithub\u002FYorko\u002Fmlcourse.ai\u002Fblob\u002Fmain\u002Fjupyter_chinese\u002Ftopic08-%E9%9A%8F%E6%9C%BA%E6%A2%AF%E5%BA%A6%E4%B8%8B%E9%99%8D%E5%92%8C%E7%8B%AC%E7%83%AD%E7%BC%96%E7%A0%81.ipynb), [Kaggle Notebook](https:\u002F\u002Fwww.kaggle.com\u002Fkashnitsky\u002Ftopic-8-online-learning-and-vowpal-wabbit)\n9. Time Series Analysis with Python, part 1 [:uk:](https:\u002F\u002Fmlcourse.ai\u002Fbook\u002Ftopic09\u002Ftopic9_part1_time_series_python.html) [:ru:](https:\u002F\u002Fhabrahabr.ru\u002Fcompany\u002Fods\u002Fblog\u002F327242\u002F) [:cn:](http:\u002F\u002Fnbviewer.ipython.org\u002Furls\u002Fraw.github.com\u002FYorko\u002Fmlcourse.ai\u002Fmain\u002Fjupyter_chinese\u002Ftopic09-%E6%97%B6%E9%97%B4%E5%BA%8F%E5%88%97%E5%A4%84%E7%90%86%E4%B8%8E%E5%BA%94%E7%94%A8.ipynb). Predicting future with Facebook Prophet, part 2 [:uk:](https:\u002F\u002Fmlcourse.ai\u002Fbook\u002Ftopic09\u002Ftopic9_part2_facebook_prophet.html), [:cn:](http:\u002F\u002Fnbviewer.ipython.org\u002Furls\u002Fraw.github.com\u002FYorko\u002Fmlcourse.ai\u002Fmain\u002Fjupyter_chinese\u002Ftopic09-%E6%97%B6%E9%97%B4%E5%BA%8F%E5%88%97%E5%A4%84%E7%90%86%E4%B8%8E%E5%BA%94%E7%94%A8.ipynb) Kaggle Notebooks: [part1](https:\u002F\u002Fwww.kaggle.com\u002Fkashnitsky\u002Ftopic-9-part-1-time-series-analysis-in-python), [part2](https:\u002F\u002Fwww.kaggle.com\u002Fkashnitsky\u002Ftopic-9-part-2-time-series-with-facebook-prophet)\n10. Gradient Boosting [:uk:](https:\u002F\u002Fmlcourse.ai\u002Fbook\u002Ftopic10\u002Ftopic10_gradient_boosting.html) [:ru:](https:\u002F\u002Fhabrahabr.ru\u002Fcompany\u002Fods\u002Fblog\u002F327250\u002F), [:cn:](https:\u002F\u002Fnbviewer.jupyter.org\u002Fgithub\u002FYorko\u002Fmlcourse.ai\u002Fblob\u002Fmain\u002Fjupyter_chinese\u002Ftopic05-%E9%9B%86%E6%88%90%E5%AD%A6%E4%B9%A0%E5%92%8C%E9%9A%8F%E6%9C%BA%E6%A3%AE%E6%9E%97%E6%96%B9%E6%B3%95.ipynb), [Kaggle Notebook](https:\u002F\u002Fwww.kaggle.com\u002Fkashnitsky\u002Ftopic-10-gradient-boosting)\n\n### Lectures\nVideo lectures are uploaded to [this](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=QKTuw4PNOsU&list=PLVlY_7IJCMJeRfZ68eVfEcu-UcN9BbwiX) YouTube playlist.\nIntroduction, [video](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=DrohHdQa8u8), [slides](https:\u002F\u002Fwww.slideshare.net\u002Ffestline\u002Fmlcourseai-fall2019-live-session-0)\n\n1. Exploratory data analysis with Pandas, [video](https:\u002F\u002Fyoutu.be\u002FfwWCw_cE5aI)\n2. Visualization, main plots for EDA, [video](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=WNoQTNOME5g)\n3. Decision trees: [theory](https:\u002F\u002Fyoutu.be\u002FH4XlBTPv5rQ) and [practical part](https:\u002F\u002Fyoutu.be\u002FRrVYO6Td9Js)\n4. Logistic regression: [theoretical foundations](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=l3jiw-N544s), [practical part](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=7o0SWgY89i8) (baselines in the \"Alice\" competition)\n5. Ensembles and Random Forest – [part 1](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=neXJL-AqI_c). Classification metrics – [part 2](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=aBOMYqGUlWQ). Example of a business task, predicting a customer payment – [part 3](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=FmKU-1LZGoE)\n6. Linear regression and regularization - [theory](https:\u002F\u002Fyoutu.be\u002Fne-MfRfYs_c), LASSO & Ridge, LTV prediction - [practice](https:\u002F\u002Fyoutu.be\u002FB8yIaIEMyIc)\n7. Unsupervised learning - [Principal Component Analysis](https:\u002F\u002Fyoutu.be\u002F-AswHf7h0I4) and [Clustering](https:\u002F\u002Fyoutu.be\u002FeVplCo-w4XE)\n8. Stochastic Gradient Descent for classification and regression - [part 1](https:\u002F\u002Fyoutu.be\u002FEUSXbdzaQE8), part 2 TBA\n9. Time series analysis with Python (ARIMA, Prophet) - [video](https:\u002F\u002Fyoutu.be\u002F_9lBwXnbOd8)\n10. Gradient boosting: basic ideas - [part 1](https:\u002F\u002Fyoutu.be\u002Fg0ZOtzZqdqk), key ideas behind Xgboost, LightGBM, and CatBoost + practice - [part 2](https:\u002F\u002Fyoutu.be\u002FV5158Oug4W8)\n\n### Assignments\n\nThe following are demo-assignments. Additionally, within the [\"Bonus Assignments\" tier](https:\u002F\u002Fwww.patreon.com\u002Fods_mlcourse) you can get access to non-demo assignments.\n\n1. Exploratory data analysis with Pandas, [nbviewer](https:\u002F\u002Fnbviewer.jupyter.org\u002Fgithub\u002FYorko\u002Fmlcourse.ai\u002Fblob\u002Fmain\u002Fjupyter_english\u002Fassignments_demo\u002Fassignment01_pandas_uci_adult.ipynb?flush_cache=true), [Kaggle Notebook](https:\u002F\u002Fwww.kaggle.com\u002Fkashnitsky\u002Fassignment-1-pandas-and-uci-adult-dataset), [solution](https:\u002F\u002Fwww.kaggle.com\u002Fkashnitsky\u002Fa1-demo-pandas-and-uci-adult-dataset-solution)\n2. Analyzing cardiovascular disease data, [nbviewer](https:\u002F\u002Fnbviewer.jupyter.org\u002Fgithub\u002FYorko\u002Fmlcourse.ai\u002Fblob\u002Fmain\u002Fjupyter_english\u002Fassignments_demo\u002Fassignment02_analyzing_cardiovascular_desease_data.ipynb?flush_cache=true), [Kaggle Notebook](https:\u002F\u002Fwww.kaggle.com\u002Fkashnitsky\u002Fassignment-2-analyzing-cardiovascular-data), [solution](https:\u002F\u002Fwww.kaggle.com\u002Fkashnitsky\u002Fa2-demo-analyzing-cardiovascular-data-solution)\n3. Decision trees with a toy task and the UCI Adult dataset, [nbviewer](https:\u002F\u002Fnbviewer.jupyter.org\u002Fgithub\u002FYorko\u002Fmlcourse.ai\u002Fblob\u002Fmain\u002Fjupyter_english\u002Fassignments_demo\u002Fassignment03_decision_trees.ipynb?flush_cache=true), [Kaggle Notebook](https:\u002F\u002Fwww.kaggle.com\u002Fkashnitsky\u002Fassignment-3-decision-trees), [solution](https:\u002F\u002Fwww.kaggle.com\u002Fkashnitsky\u002Fa3-demo-decision-trees-solution)\n4. Sarcasm detection, [Kaggle Notebook](https:\u002F\u002Fwww.kaggle.com\u002Fkashnitsky\u002Fa4-demo-sarcasm-detection-with-logit), [solution](https:\u002F\u002Fwww.kaggle.com\u002Fkashnitsky\u002Fa4-demo-sarcasm-detection-with-logit-solution). Linear Regression as an optimization problem, [nbviewer](https:\u002F\u002Fnbviewer.jupyter.org\u002Fgithub\u002FYorko\u002Fmlcourse.ai\u002Fblob\u002Fmain\u002Fjupyter_english\u002Fassignments_demo\u002Fassignment04_linreg_optimization.ipynb?flush_cache=true), [Kaggle Notebook](https:\u002F\u002Fwww.kaggle.com\u002Fkashnitsky\u002Fassignment-4-linear-regression-as-optimization)\n5. Logistic Regression and Random Forest in the credit scoring problem, [nbviewer](https:\u002F\u002Fnbviewer.jupyter.org\u002Fgithub\u002FYorko\u002Fmlcourse.ai\u002Fblob\u002Fmain\u002Fjupyter_english\u002Fassignments_demo\u002Fassignment05_logit_rf_credit_scoring.ipynb?flush_cache=true), [Kaggle Notebook](https:\u002F\u002Fwww.kaggle.com\u002Fkashnitsky\u002Fassignment-5-logit-and-rf-for-credit-scoring), [solution](https:\u002F\u002Fwww.kaggle.com\u002Fkashnitsky\u002Fa5-demo-logit-and-rf-for-credit-scoring-sol)\n6. Exploring OLS, Lasso and Random Forest in a regression task, [nbviewer](https:\u002F\u002Fnbviewer.jupyter.org\u002Fgithub\u002FYorko\u002Fmlcourse.ai\u002Fblob\u002Fmain\u002Fjupyter_english\u002Fassignments_demo\u002Fassignment06_regression_wine.ipynb?flush_cache=true), [Kaggle Notebook](https:\u002F\u002Fwww.kaggle.com\u002Fkashnitsky\u002Fassignment-6-linear-models-and-rf-for-regression), [solution](https:\u002F\u002Fwww.kaggle.com\u002Fkashnitsky\u002Fa6-demo-regression-solution)\n7. Unsupervised learning, [nbviewer](https:\u002F\u002Fnbviewer.jupyter.org\u002Fgithub\u002FYorko\u002Fmlcourse.ai\u002Fblob\u002Fmain\u002Fjupyter_english\u002Fassignments_demo\u002Fassignment07_unsupervised_learning.ipynb?flush_cache=true), [Kaggle Notebook](https:\u002F\u002Fwww.kaggle.com\u002Fkashnitsky\u002Fassignment-7-unupervised-learning), [solution](https:\u002F\u002Fwww.kaggle.com\u002Fkashnitsky\u002Fa7-demo-unsupervised-learning-solution)\n8. Implementing online regressor, [nbviewer](https:\u002F\u002Fnbviewer.jupyter.org\u002Fgithub\u002FYorko\u002Fmlcourse.ai\u002Fblob\u002Fmain\u002Fjupyter_english\u002Fassignments_demo\u002Fassignment08_implement_sgd_regressor.ipynb?flush_cache=true), [Kaggle Notebook](https:\u002F\u002Fwww.kaggle.com\u002Fkashnitsky\u002Fassignment-8-implementing-online-regressor), [solution](https:\u002F\u002Fwww.kaggle.com\u002Fkashnitsky\u002Fa8-demo-implementing-online-regressor-solution)\n9. Time series analysis, [nbviewer](https:\u002F\u002Fnbviewer.jupyter.org\u002Fgithub\u002FYorko\u002Fmlcourse.ai\u002Fblob\u002Fmain\u002Fjupyter_english\u002Fassignments_demo\u002Fassignment09_time_series.ipynb?flush_cache=true), [Kaggle Notebook](https:\u002F\u002Fwww.kaggle.com\u002Fkashnitsky\u002Fassignment-9-time-series-analysis), [solution](https:\u002F\u002Fwww.kaggle.com\u002Fkashnitsky\u002Fa9-demo-time-series-analysis-solution)\n10. Beating baseline in a competition, [Kaggle Notebook](https:\u002F\u002Fwww.kaggle.com\u002Fkashnitsky\u002Fassignment-10-gradient-boosting-and-flight-delays)\n\n### Kaggle competitions\n\n1. Catch Me If You Can: Intruder Detection through Webpage Session Tracking. [Kaggle Inclass](https:\u002F\u002Fwww.kaggle.com\u002Fc\u002Fcatch-me-if-you-can-intruder-detection-through-webpage-session-tracking2)\n2. Predicting popularity of a Medium article. [Kaggle Inclass](https:\u002F\u002Fwww.kaggle.com\u002Fc\u002Fhow-good-is-your-medium-article)\n3. DotA 2 winner prediction. [Kaggle Inclass](https:\u002F\u002Fwww.kaggle.com\u002Fc\u002Fmlcourse-dota2-win-prediction)\n\n### Building course material as a Jupyter Book\n\nWe are using [Jupyter Book v1](https:\u002F\u002Fjupyterbook.org\u002Fv1\u002Fstart\u002Fbuild.html) to build the book. \n\n1. Install [uv](https:\u002F\u002Fgithub.com\u002Fastral-sh\u002Fuv) for dependency management: `pip install uv`;\n1. Run `uv sync` to install project dependencies, or `uv lock --upgrade` to update them;\n1. Run `uv run jb build mlcourse_ai_jupyter_book` (_note: this works with Jupyter Book v1_) – this will take a while, the output is found in the [mlcourse_ai_jupyter_book\u002F_build](mlcourse_ai_jupyter_book\u002F_build) folder. Namely, [mlcourse_ai_jupyter_book\u002F_build\u002Fhtml\u002Findex.html](mlcourse_ai_jupyter_book\u002F_build\u002Fhtml\u002Findex.html) is what gets rendered at the [mlcourse.ai](https:\u002F\u002Fmlcourse.ai) main page. \n\nThis will show the path to your local file with the course material, e.g. `file:\u002F\u002F\u002FUsers\u002Fnickname\u002FDocuments\u002Fmlcourse.ai\u002Fmlcourse_ai_jupyter_book\u002F_build\u002Fhtml\u002Findex.html`. You can open it in your browser to see the course material locally.\n\n### Citing mlcourse.ai\n\nIf you happen to cite [mlcourse.ai](https:\u002F\u002Fmlcourse.ai) in your work, you can use this BibTeX record:\n\n```\n@misc{mlcourse_ai,\n    author = {Kashnitsky, Yury},\n    title = {mlcourse.ai – Open Machine Learning Course},\n    year = {2020},\n    publisher = {GitHub},\n    journal = {GitHub repository},\n    howpublished = {\\url{https:\u002F\u002Fgithub.com\u002FYorko\u002Fmlcourse.ai}},\n}\n```\n","mlcourse.ai 是由 OpenDataScience (ods.ai) 提供的开放机器学习课程，旨在为学习者提供理论与实践相结合的学习体验。该项目以 Python 为主要编程语言，涵盖算法、数据分析、数据科学等多个领域，并通过 Jupyter Notebook 形式提供丰富的教学内容和实战练习。它适合希望系统性学习机器学习基础知识并参与实际项目（如 Kaggle 比赛）的学生或专业人士使用。此外，还提供了额外付费的进阶作业包供更深入学习之用。","2026-06-11 03:24:03","top_topic"]