Tutorials refactoring

ref:d5d147675f6a45512dbcfa0e60855dfe3ae671bd

Author: sab

README.md

@@ -1,47 +1,82 @@
-# Tutorials
+# CatBoost tutorials
 
-## Python tutorials
+## Basic
 
-* Main CatBoost tutorial with base features demonstration:
-    * [Python Tutorial](catboost_python_tutorial.ipynb)
-        * This tutorial shows some base cases of using catboost, such as model training, cross-validation and predicting, as well as some useful features like early stopping,  snapshot support, feature importances and parameters tuning.
+It's better to start CatBoost exploring from this basic tutorials.
 
-* CatBoost model analysis tutorials:
-    * [Object Importance Tutorial](advanced_tutorials/catboost_object_importance_tutorial.ipynb)
-        * This tutorial shows how to evaluate importances of the train objects for test objects. And with using of importance scores detect noisy train objects.
+### Python
 
-    * [SHAP Values Tutorial](advanced_tutorials/shap_values_tutorial.ipynb)
-        * This tutorial shows how to use [SHAP](https://github.com/slundberg/shap) python-package to get and visualize feature importances.
+* [Python Tutorial](python_tutorial.ipynb)
+    * This tutorial shows some base cases of using CatBoost, such as model training, cross-validation and predicting, as well as some useful features like early stopping,  snapshot support, feature importances and parameters tuning.
+* [Python Tutorial with task](python_tutorial_with_tasks.ipynb)
+    * There are 17 questions in this tutorial. Try answering all of them, this will help you to learn how to use the library.
 
-* CatBoost performance at different competitions:
-    * [Kaggle Paribas Tutorial](advanced_tutorials/kaggle_paribas.ipynb)
-        * This tutorial shows how to get to a 9th place on paribas competition with only few lines of code and training a CatBoost model.
+### R
 
-    * [ML Boot Camp Tutorial](advanced_tutorials/mlbootcamp_v_tutorial.ipynb)
-        * This is an actual 7th place solution by Mikhail Pershin. Solution is very simple and is based on CatBoost.
+* [R Tutorial](r_tutorial.ipynb)
+    * This tutorial shows how to convert your data to CatBoost Pool, how to train a model and how to make cross validation and parameter tunning.
 
-* CatBoost and TensorFlow:
-    * [CatBoost & TensorFlow Tutorial](advanced_tutorials/quora_catboost_w2v.ipynb)
-        * This tutorial shows how to use CatBoost together with TensorFlow if you have text as input data.
+### Command line
 
-* CatBoost and CoreML:
-    * [CatBoost CoreML Tutorial](advanced_tutorials/catboost_coreml_export_tutorial.ipynb)
-        * This tutorial shows how to convert CatBoost model to CoreML format and use it on an iPhone.
+* [Command Line Tutorial](cmdline_tutorial.md)
+    * This tutorial shows how to train and apply model with the command line tool.
 
-## R tutorials
+## Classification
 
-* Main CatBoost tutorial with base features demonstration:
-    * [R Tutorial](catboost_r_tutorial.ipynb)
-        * This tutorial shows how to convert your data to CatBoost Pool, how to train a model and how to make cross validation and parameter tunning.
+* [Classification Tutorial](classification/classification_tutorial.ipynb)
+    * Here is an example for CatBoost to solve binary classification and multi-classification problems.
 
-## Command line tutorials
+## Ranking
+* [Ranking Tutorial](ranking/ranking_tutorial.ipynb)
+    * CatBoost is learning to rank on Microsoft dataset (msrank).
 
-* Main CatBoost tutorial with base features demonstration:
-    * [Command Line Tutorial](catboost_cmdline_tutorial.md)
-        * This tutorial shows how to train and apply model with the command line tool.
+## Feature selection
+* [Feature selection Tutorial](feature_selection/eval_tutorial.ipynb)
+    * This tutorial shows how to make feature evaluation with CatBoost and explore learning rate.
 
-## Custom loss tutorial
+## Model analysis
 
-* Adding custom per-object error function tutorial:
-    * [Custom Metrics Tutorial](advanced_tutorials/catboost_custom_metric_tutorial.md)
-        * This tutorial shows how to add custom per-object metrics.
+* [Object Importance Tutorial](model_analysis/object_importance_tutorial.ipynb)
+    * This tutorial shows how to evaluate importances of the train objects for test objects. And with using of importance scores detect noisy train objects.
+
+* [SHAP Values Tutorial](model_analysis/shap_values_tutorial.ipynb)
+    * This tutorial shows how to use [SHAP](https://github.com/slundberg/shap) python-package to get and visualize feature importances.
+
+## Custom loss
+
+* [Custom Metrics Tutorial](custom_loss/custom_metric_tutorial.md)
+    * This tutorial shows how to add custom per-object metrics.
+
+## Apply model
+
+* [CatBoost CoreML Tutorial](apply_model/coreml_export_tutorial.ipynb)
+    * Explore this tutorial to learn how to convert CatBoost model to CoreML format and use it on any iOS device.
+
+* [Export CatBoost Model as C++ code Tutorial](apply_model/model_export_as_cpp_code_tutorial.md)
+    * Catboost model could be saved as standalone C++ code.
+
+* [Export CatBoost Model as Python code Tutorial](apply_model/model_export_as_python_code_tutorial.md)
+    * Catboost model could be saved as standalone Python code.
+
+## Competition examples
+
+* [Kaggle Paribas Competition Tutorial](competition_examples/kaggle_paribas.ipynb)
+    * This tutorial shows how to get to a 9th place on Kaggle Paribas competition with only few lines of code and training a CatBoost model.
+
+* [ML Boot Camp V Competition Tutorial](competition_examples/mlbootcamp_v_tutorial.ipynb)
+    * This is an actual 7th place solution by Mikhail Pershin. Solution is very simple and is based on CatBoost.
+
+* [CatBoost & TensorFlow Tutorial](competition_examples/quora_w2v.ipynb)
+    * This tutorial shows how to use CatBoost together with TensorFlow on Kaggle Quora Question Pairs competition if you have text as input data.
+
+## Events
+
+* [PyData NYC tutorial](events/pydata_nyc_oct_19_2018.ipynb)
+    * Tutorial from PyData New York, October 19, 2018.
+
+* [PyData LA tutorial](events/pydata_la_oct_21_2018.ipynb)
+    * Tutorial from PyData Los Angeles, October 21, 2018.
+
+## Tutorials on Russian
+
+* Find tutorials on Russian language on the separate [page](ru/README.md).

advanced_tutorials/catboost_amazon_aws_tutorial.md

@@ -0,0 +1,10 @@
+# Apply model
+
+* [CatBoost CoreML Tutorial](apply_model/coreml_export_tutorial.ipynb)
+    * Explore this tutorial to learn how to convert CatBoost model to CoreML format and use it on any iOS device.
+
+* [Export CatBoost Model as C++ code Tutorial](apply_model/model_export_as_cpp_code_tutorial.md)
+    * Catboost model could be saved as standalone C++ code.
+
+* [Export CatBoost Model as Python code Tutorial](apply_model/model_export_as_python_code_tutorial.md)
+    * Catboost model could be saved as standalone Python code.

advanced_tutorials/ru/README.md

@@ -0,0 +1,4 @@
+# Classification
+
+* [Classification Tutorial](classification/classification_tutorial.ipynb)
+    * Here is an example for CatBoost to solve binary classification and multi-classification problems.

apply_model/README.md

@@ -0,0 +1,10 @@
+# Competition examples
+
+* [Kaggle Paribas Competition Tutorial](competition_examples/kaggle_paribas.ipynb)
+    * This tutorial shows how to get to a 9th place on Kaggle Paribas competition with only few lines of code and training a CatBoost model.
+
+* [ML Boot Camp V Competition Tutorial](competition_examples/mlbootcamp_v_tutorial.ipynb)
+    * This is an actual 7th place solution by Mikhail Pershin. Solution is very simple and is based on CatBoost.
+
+* [CatBoost & TensorFlow Tutorial](competition_examples/quora_w2v.ipynb)
+    * This tutorial shows how to use CatBoost together with TensorFlow on Kaggle Quora Question Pairs competition if you have text as input data.

advanced_tutorials/catboost_coreml_export_tutorial.ipynb renamed to apply_model/coreml_export_tutorial.ipynb

@@ -0,0 +1,4 @@
+# Custom loss
+
+* [Custom Metrics Tutorial](custom_loss/custom_metric_tutorial.md)
+    * This tutorial shows how to add custom per-object metrics.

advanced_tutorials/catboost_model_export_as_cpp_code_tutorial.md renamed to apply_model/model_export_as_cpp_code_tutorial.md

@@ -0,0 +1,3 @@
+# Feature selection
+* [Feature selection Tutorial](feature_selection/eval_tutorial.ipynb)
+    * This tutorial shows how to make feature evaluation with CatBoost and explore learning rate.

advanced_tutorials/catboost_model_export_as_python_code_tutorial.md renamed to apply_model/model_export_as_python_code_tutorial.md

@@ -0,0 +1,7 @@
+# Model analysis
+
+* [Object Importance Tutorial](model_analysis/object_importance_tutorial.ipynb)
+    * This tutorial shows how to evaluate importances of the train objects for test objects. And with using of importance scores detect noisy train objects.
+
+* [SHAP Values Tutorial](model_analysis/shap_values_tutorial.ipynb)
+    * This tutorial shows how to use [SHAP](https://github.com/slundberg/shap) python-package to get and visualize feature importances.

classification/README.md

@@ -0,0 +1,3 @@
+# Ranking
+* [Ranking Tutorial](ranking/ranking_tutorial.ipynb)
+    * CatBoost is learning to rank on Microsoft dataset (msrank).

classification_tutorial.ipynb renamed to classification/classification_tutorial.ipynb

@@ -0,0 +1,7 @@
+# Туториалы CatBoost на русском языке
+* [Туториал Kaggle Amazon](kaggle_amazon_tutorial_ru.ipynb)
+    * Туториал с демонстрацией основного функционала библиотеки на датасете Amazon Employee Access Challenge.
+* [ML Session, Новосибирск 2018](ml_session_2018_tutorial_ru.ipynb)
+    * Туториал c мероприятия [ML Session](https://events.yandex.ru/events/meetings/19-april-2018/) прошедшего 19 Апреля 2018 в Новосибирске.
+* [CatBoost и ClickHouse, Москва 2017](catboost_with_clickhouse_tutorial_ru.ipynb)
+    * Туториал с мероприятия [Опенсорс в Яндексе: CatBoost и ClickHouse](https://events.yandex.ru/events/ClickHouse/30-november-2017/) прошедшего 30 Ноября 2017 в Москве.