upd Readme, re-build jupyterbook, update poetry deps, fix Yorko#705

.pre-commit-config.yaml

@@ -4,6 +4,7 @@ repos:
     rev: v4.0.1
     hooks:
       - id: check-added-large-files
+        args: ['--maxkb=1024']
       - id: check-docstring-first
       - id: check-json
       - id: check-xml

README.md

@@ -11,7 +11,7 @@
 
 </div>
 
-[mlcourse.ai](https://mlcourse.ai) is an open Machine Learning course by [OpenDataScience (ods.ai)](https://ods.ai/), led by [Yury Kashnitsky (yorko)](https://yorko.github.io/). 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, you go through math formulae in lectures, and practice with Kaggle Inclass competitions. Currently, the course is in a **self-paced mode**. Check out a thorough [Roadmap](roadmap) guiding you through the self-paced mlcourse.ai. The Russian version of the course is resurrected and will be led by [Petr Ermakov](https://www.linkedin.com/in/ermakovpetr/). If you speak Russian, please refer to [this Open ML course](https://ods.ai/tracks/open-ml-course) launching on Feb 1st, 2022.
+[mlcourse.ai](https://mlcourse.ai) is an open Machine Learning course by [OpenDataScience (ods.ai)](https://ods.ai/), led by [Yury Kashnitsky (yorko)](https://yorko.github.io/). 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 a 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://mlcourse.ai).
 
 __Bonus:__
 Additionally, you can purchase a Bonus Assignments pack with the best non-demo versions of [mlcourse.ai](https://mlcourse.ai/) assignments. Select the ["Bonus Assignments" tier](https://www.patreon.com/ods_mlcourse). Refer to the details of the deal on the main page [mlcourse.ai](https://mlcourse.ai/).
@@ -70,12 +70,6 @@ The following are demo-assignments. Additionally, within the ["Bonus Assignments
 1. Catch Me If You Can: Intruder Detection through Webpage Session Tracking. [Kaggle Inclass](https://www.kaggle.com/c/catch-me-if-you-can-intruder-detection-through-webpage-session-tracking2)
 2. DotA 2 winner prediction. [Kaggle Inclass](https://www.kaggle.com/c/mlcourse-dota2-win-prediction)
 
-### Russian version of the course
-
-The course originated in [OpenDataScience](https://ods.ai/), at that time in 2017 (well, and still), mostly Russian-speaking community. Hence the first couple of sessions were held in Russian. Then, later, the course launched in English and resulted in what you see here and on [mlcourse.ai](https://mlcourse.ai).
-
-The Russian version of the course is resurrected and is led by [Petr Ermakov](https://www.linkedin.com/in/ermakovpetr/). If you speak Russian, please refer to [this Open ML course](https://ods.ai/tracks/open-ml-course) launching on **Feb 1st, 2022.**
-
 ### Citing mlcourse.ai
 
 If you happen to cite [mlcourse.ai](https://mlcourse.ai) in your work, you can use this BibTeX record:

jupyter_russian/topic05_bagging_rf/topic5_part3_feature_importance.ipynb

@@ -76,7 +76,7 @@
     "rc(\"font\", **font)\n",
     "import numpy as np\n",
     "import pandas as pd\n",
-    "from sklearn.ensemble.forest import RandomForestRegressor"
+    "from sklearn.ensemble import RandomForestRegressor"
    ]
   },
   {
@@ -140,7 +140,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "Python 3 (ipykernel)",
    "language": "python",
    "name": "python3"
   },
@@ -154,7 +154,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.6.1"
+   "version": "3.10.4"
   }
  },
  "nbformat": 4,

mlcourse_ai_jupyter_book/book/extra/rating.md

@@ -328,7 +328,7 @@ According to the [final rating](https://goo.gl/vMHdED). The session took place f
 1. Alexey Vinogradov, [LinkedIn](https://www.linkedin.com/in/aleksey-vinogradov-02846625/), [email](mailto:[email protected])
 1. Andrei Rem, [GitHub](http://github.com/andreirem), [LinkedIn](https://www.linkedin.com/in/andrei-rem), [email](mailto:[email protected])
 1. Svetlana Sudarkova, [GitHub](https://github.com/sudarkova), [LinkedIn](https://www.linkedin.com/in/sudarkova), [email](mailto:[email protected])
-1. Aleksandr Korotkov, [LinkedIn](https://www.linkedin.com/in/alexandr-korotkov/), [email](mailto:[email protected])
+1. Aleksandr Korotkov, [LinkedIn](https://www.linkedin.com/in/krotix/), [email](mailto:[email protected])
 1. Haowen Jiang, [GitHub](https://github.com/Jianghaowen/), [LinkedIn](https://www.linkedin.com/in/jiang-haowen-618970158/), [email](mailto:[email protected])
 1. Ackerley Tng, [GitHub](https://github.com/ackerleytng/), [LinkedIn](https://www.linkedin.com/in/ackerleytng/), [email](mailto:[email protected])
 1. Aleksandr Kuptsov, [GitHub](https://github.com/Kommunarus), [LinkedIn](http://www.linkedin.com/in/Kuptsov-Aleksandr), [email](mailto:[email protected])

mlcourse_ai_jupyter_book/book/index.md

@@ -7,7 +7,7 @@
 
 
 
-[mlcourse.ai](https://mlcourse.ai) is an open Machine Learning course by [OpenDataScience (ods.ai)](https://ods.ai/), led by [Yury Kashnitsky (yorko)](https://yorko.github.io/). 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, you go through math formulae in lectures, and practice with Kaggle Inclass competitions. Currently, the course is in a **self-paced mode**. Here we guide you through the self-paced [mlcourse.ai](https://mlcourse.ai).
+[mlcourse.ai](https://mlcourse.ai) is an open Machine Learning course by [OpenDataScience (ods.ai)](https://ods.ai/), led by [Yury Kashnitsky (yorko)](https://yorko.github.io/). 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 a 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://mlcourse.ai).
 
 In the following [short video](https://youtu.be/CPlYV_DryEo) we discuss how to best approach the course material:
 
@@ -69,11 +69,3 @@ If you scroll down here on the left, you see the "About the course" section with
  - One of the motivating features of the the past mlcourse.ai sessions was the leaderboard. Top 100 participants of each session are listed on the [Rating](rating) page;
  - The [Resources](resources) page lists other resources constituting the course, e.g. YouTube playlists or Medium/Habr.com articles written in the past;
  - Authors and some of the mlcourse.ai contributors (there were too many to list all of them) are listed on the [Contributors](contributors) page. Acknowledgements are there as well.
-
-<!-- - Lastly, the [Support](support) page -->
-
-## Russian version of the course
-
-The course originated in [OpenDataScience](https://ods.ai/), at that time in 2017 (well, and still), mostly Russian-speaking community. Hence the first couple of sessions were held in Russian. Then, later, the course launched in English and resulted in what you see here.
-
-The Russian version of the course is resurrected and will be led by [Petr Ermakov](https://www.linkedin.com/in/ermakovpetr/). If you speak Russian, please refer to [this Open ML course](https://ods.ai/tracks/open-ml-course) launching on **Feb 1st, 2022.**