add ToC to each lecture

mlcourse_ai_jupyter_book/book/topic01/topic01_pandas_data_analysis.md

@@ -29,9 +29,8 @@ _Source: Getty Images_
 
 ## Article outline
 
-1. [Demonstration of the main Pandas methods](#1-demonstration-of-the-main-pandas-methods)
-2. [First attempt at predicting telecom churn](#2-first-attempt-at-predicting-telecom-churn)
-3. [Useful resources](#3-useful-resources)
+```{contents}
+```
 
 ## 1. Demonstration of the main Pandas methods
 

mlcourse_ai_jupyter_book/book/topic02/topic02_additional_seaborn_matplotlib_plotly.md

@@ -27,11 +27,8 @@ Author: [Egor Polusmak](https://www.linkedin.com/in/egor-polusmak/). Translated
 
 ## Article outline
 
-1. [Dataset](1-dataset)
-2. [DataFrame.plot()](2-dataframe-plot)
-3. [Seaborn](3-seaborn)
-4. [Plotly](4-plotly)
-5. [Useful resources](5-useful-resources)
+```{contents}
+```
 
 ## 1. Dataset
 

mlcourse_ai_jupyter_book/book/topic02/topic02_visual_data_analysis.md

@@ -33,19 +33,8 @@ In this article, we are going to get hands-on experience with visual exploration
 
 ## Article outline
 
-1. [Dataset](1-dataset)
-2. [Univariate visualization](2-univariate-visualization)
-    * 2.1 [Quantitative features](21-quantitative-features)
-    * 2.2 [Categorical and binary features](22-categorical-and-binary-features)
-3. [Multivariate visualization](3-multivariate-visualization)
-    * 3.1 [Quantitative vs. Quantitative](31-quantitative-vs-quantitative)
-    * 3.2 [Quantitative vs. Categorical](32-quantitative-vs-categorical)
-    * 3.3 [Categorical vs. Categorical](33-categorical-vs-categorical)
-4. [Whole dataset visualizations](4-whole-dataset-visualizations)
-    * 4.1 [Naive approach](41-a-naive-approach)
-    * 4.2 [Dimensionality reduction](42-dimensionality-reduction)
-    * 4.3 [t-SNE](43-t-SNE)
-5. [Useful resources](5-useful-resources)
+```{contents}
+```
 
 ## 1. Dataset
 

mlcourse_ai_jupyter_book/book/topic03/topic03_decision_trees_kNN.md

@@ -22,13 +22,8 @@ Author: [Yury Kashnitsky](https://yorko.github.io). Translated and edited by [Ch
 
 ## Article outline
 
-1. [Introduction](introduction)
-2. [Decision Tree](decision-tree)
-3. [Nearest Neighbors Method](nearest-neighbors-nethod)
-4. [Choosing Model Parameters and Cross-Validation](choosing-model-parameters-and-cross-validation)
-5. [Application Examples and Complex Cases](application-examples-and-complex-cases)
-6. [Pros and Cons of Decision Trees and the Nearest Neighbors Method](pros-and-cons-of-decision-trees-and-the-nearest-neighbors-method)
-7. [Useful resources](useful-resources)
+```{contents}
+```
 
 ## 1. Introduction
 

mlcourse_ai_jupyter_book/book/topic04/topic4_linear_models_part1_mse_likelihood_bias_variance.md

@@ -24,11 +24,9 @@ Author: [Pavel Nesterov](http://pavelnesterov.info/). Translated and edited by [
 
 
 ## Article outline
-1. [Introduction](introduction)
-2. [Maximum Likelihood Estimation](maximum-likelihood-estimation)
-3. [Bias-Variance Decomposition](bias-variance-decomposition)
-4. [Regularization of Linear Regression](regularization-of-linear-regression)
-5. [Useful resources](useful-resources)
+
+```{contents}
+```
 
 
 ## 1. Introduction

mlcourse_ai_jupyter_book/book/topic04/topic4_linear_models_part2_logit_likelihood_learning.md

@@ -25,11 +25,9 @@ kernelspec:
 Author: [Yury Kashnitsky](https://yorko.github.io). Translated and edited by [Christina Butsko](https://www.linkedin.com/in/christinabutsko/), [Nerses Bagiyan](https://www.linkedin.com/in/nersesbagiyan/), [Yulia Klimushina](https://www.linkedin.com/in/yuliya-klimushina-7168a9139), and [Yuanyuan Pao](https://www.linkedin.com/in/yuanyuanpao/). This material is subject to the terms and conditions of the [Creative Commons CC BY-NC-SA 4.0](https://creativecommons.org/licenses/by-nc-sa/4.0/) license. Free use is permitted for any non-commercial purpose.
 
 ## Article outline
-1. [Linear Classifier](linear-classifier)
-2. [Logistic Regression as a Linear Classifier](logistic-regression-as-a-linear-classifier)
-3. [Maximum Likelihood Estimation and Logistic Regression](maximum-likelihood-estimation-and-logistic-regression)
-4. [$L_2$-Regularization of Logistic Loss](l-2-regularization-of-logistic-loss)
-5. [Useful resources](useful-resources)
+
+```{contents}
+```
 
 ## 1. Linear Classifier
 

mlcourse_ai_jupyter_book/book/topic04/topic4_linear_models_part3_regul_example.md

@@ -24,6 +24,11 @@ kernelspec:
 
 Author: [Yury Kashnitsky](https://yorko.github.io). Translated and edited by [Christina Butsko](https://www.linkedin.com/in/christinabutsko/), [Nerses Bagiyan](https://www.linkedin.com/in/nersesbagiyan/), [Yulia Klimushina](https://www.linkedin.com/in/yuliya-klimushina-7168a9139), and [Yuanyuan Pao](https://www.linkedin.com/in/yuanyuanpao/). This material is subject to the terms and conditions of the [Creative Commons CC BY-NC-SA 4.0](https://creativecommons.org/licenses/by-nc-sa/4.0/) license. Free use is permitted for any non-commercial purpose.
 
+## Article outline
+
+```{contents}
+```
+
 In the first article, we demonstrated how polynomial features allow linear models to build nonlinear separating surfaces. Let's now show this visually.
 
 Let's see how regularization affects the quality of classification on a dataset on microchip testing from Andrew Ng's course on machine learning. We will use logistic regression with polynomial features and vary the regularization parameter $C$. First, we will see how regularization affects the separating border of the classifier and intuitively recognize under- and overfitting. Then, we will choose the regularization parameter to be numerically close to the optimal value via (`cross-validation`) and (`GridSearch`).

mlcourse_ai_jupyter_book/book/topic04/topic4_linear_models_part4_good_bad_logit_movie_reviews_XOR.md

@@ -25,10 +25,9 @@ Author: [Yury Kashnitsky](https://yorko.github.io). Translated and edited by [Ch
 
 
 ## Article outline
-1. [Analysis of IMDB movie reviews](analysis-of-imdb-movie-reviews)
-2. [A Simple Word Count](a-simple-word-count)
-3. [The XOR Problem](the-xor-problem)
-4. [Useful resources](useful-resources)
+
+```{contents}
+```
 
 ## 1. Analysis of IMDB movie reviews
 

mlcourse_ai_jupyter_book/book/topic04/topic4_linear_models_part5_valid_learning_curves.md

@@ -24,6 +24,12 @@ kernelspec:
 Author: [Yury Kashnitsky](https://yorko.github.io). Translated and edited by [Christina Butsko](https://www.linkedin.com/in/christinabutsko/), [Nerses Bagiyan](https://www.linkedin.com/in/nersesbagiyan/), [Yulia Klimushina](https://www.linkedin.com/in/yuliya-klimushina-7168a9139), and [Yuanyuan Pao](https://www.linkedin.com/in/yuanyuanpao/). This material is subject to the terms and conditions of the [Creative Commons CC BY-NC-SA 4.0](https://creativecommons.org/licenses/by-nc-sa/4.0/) license. Free use is permitted for any non-commercial purpose.
 
 
+## Article outline
+
+```{contents}
+```
+
+
 ```{code-cell} ipython3
 import warnings
 import numpy as np

mlcourse_ai_jupyter_book/book/topic05/topic5_part1_bagging.md

@@ -23,11 +23,8 @@ Authors: [Vitaliy Radchenko](https://www.linkedin.com/in/vitaliyradchenk0/), and
 
 ## Article outline
 
-1. [Ensembles](ensembles)
-2. [Bootstrapping](bootstrapping)
-3. [Bagging](bagging)
-4. [Out-of-bag error](out-of-bag-error)
-5. [Useful resources](useful-resources)
+```{contents}
+```
 
 $\DeclareMathOperator{\Var}{Var}$
 $\DeclareMathOperator{\Cov}{Cov}$

mlcourse_ai_jupyter_book/book/topic05/topic5_part2_random_forest.md

@@ -24,16 +24,8 @@ Authors: [Vitaliy Radchenko](https://www.linkedin.com/in/vitaliyradchenk0/), and
 
 ## Article outline
 
-1. [Algorithm](algorithm)
-2. [Comparison with Decision Trees and Bagging](comparison-with-decision-trees-and-bagging)
-3. [Parameters](parameters)
-4. [Variance and Decorrelation](variance-and-decorrelation)
-5. [Bias](bias)
-6. [Extremely Randomized Trees](extremely-randomized-trees)
-7. [Similarities between Random Forest and k-Nearest Neighbors](similarities-between-random-forest-and-k-nearest-neighbors)
-8. [Transformation of a dataset into a high-dimensional representation](transformation-of-a-dataset-into-a-high-dimensional-representation)
-9. [Pros and cons of random forests](pros-and-cons-of-random-forests)
-10. [Useful resources](useful-resources)
+```{contents}
+```
 
 $\DeclareMathOperator{\Var}{Var}$
 $\DeclareMathOperator{\Cov}{Cov}$

mlcourse_ai_jupyter_book/book/topic05/topic5_part3_feature_importance.md

@@ -23,11 +23,8 @@ Authors: [Vitaliy Radchenko](https://www.linkedin.com/in/vitaliyradchenk0/), [Yu
 
 ## Article outline
 
-1. [Intuition](intuition)
-2. [Illustrating permutation importance](illustrating-permutation-importance)
-3. [Sklearn Random Forest Feature Importance](sklearn-random-forest-feature-importance)
-4. [Practical example](practical-example)
-5. [Useful resources](useful-resources)
+```{contents}
+```
 
 It's quite often that you want to make out the exact reasons of the algorithm outputting a particular answer. Or at the very least to find out which input features contributed most to the result. With Random Forest, you can obtain such information quite easily.
 

mlcourse_ai_jupyter_book/book/topic06/topic6_feature_engineering_feature_selection.md

@@ -20,6 +20,12 @@ kernelspec:
 
 Author: [Arseny Kravchenko](https://arseny.info/pages/about_me.html#about_me). Translated and edited by [Christina Butsko](https://www.linkedin.com/in/christinabutsko/), [Yury Kashnitsky](https://yorko.github.io/), [Egor Polusmak](https://www.linkedin.com/in/egor-polusmak/), [Anastasia Manokhina](https://www.linkedin.com/in/anastasiiamanokhina/), [Anna Larionova](https://www.linkedin.com/in/anna-larionova-74434689/), [Evgeny Sushko](https://www.linkedin.com/in/evgenysushko/) and [Yuanyuan Pao](https://www.linkedin.com/in/yuanyuanpao/). This material is subject to the terms and conditions of the [Creative Commons CC BY-NC-SA 4.0](https://creativecommons.org/licenses/by-nc-sa/4.0/) license. Free use is permitted for any non-commercial purpose.
 
+
+## Article outline
+
+```{contents}
+```
+
 In this course, we have already seen several key machine learning algorithms. However, before moving on to the more fancy ones, we’d like to take a small detour and talk about data preparation. The well-known concept of “garbage in — garbage out” applies 100% to any task in machine learning. Any experienced professional can recall numerous times when a simple model trained on high-quality data was proven to be better than a complicated multi-model ensemble built on data that wasn’t clean.
 
 To start, I wanted to review three similar but different tasks:

mlcourse_ai_jupyter_book/book/topic07/topic7_pca_clustering.md

@@ -28,17 +28,9 @@ In this lesson, we will work with unsupervised learning methods such as Principa
 
 
 ## Article outline
-1. [Introduction](introduction)
-2. [Principal Component Analysis (PCA)](principal-component-analysis-pca)
- - [Intuition, theories, and application issues](intuition-theories-and-application-issues)
- - [Examples](examples)
-3. [Clustering](clustering)
- - [K-means](k-means)
- - [Affinity Propagation](affinity-propagation)
- - [Spectral clustering](spectral-clustering)
- - [Agglomerative clustering](agglomerative-clustering)
- - [Accuracy metrics](accuracy-metrics)
-4. [Useful links](useful-links)
+
+```{contents}
+```
 
 ## 1. Introduction