diff --git a/README.md b/README.md
index c87d13553..dab98176e 100644
--- a/README.md
+++ b/README.md
@@ -31,9 +31,9 @@ transforming parameters from the data and then transform it.
 
 * [Feature Engineering for Time Series Forecasting, Online Course](https://www.courses.trainindata.com/p/feature-engineering-for-forecasting)
 
-* [Deployment of Machine Learning Models, Online Course](https://www.udemy.com/course/deployment-of-machine-learning-models/?referralCode=D4FE5EA129FFD203CFF4)
+* [Python Feature Engineering Cookbook](https://packt.link/0ewSo)
 
-* [Python Feature Engineering Cookbook](https://packt.link/python)
+* [Feature Selection in Machine Learning with Python Book](https://leanpub.com/feature-selection-in-machine-learning)
 
 
 ## Blogs about Feature-engine
@@ -43,13 +43,6 @@ transforming parameters from the data and then transform it.
 * [Practical Code Implementations of Feature Engineering for Machine Learning with Python](https://towardsdatascience.com/practical-code-implementations-of-feature-engineering-for-machine-learning-with-python-f13b953d4bcd)
 
 
-## En Español
-
-* [Ingeniería de variables para machine learning, Curso Online](https://www.udemy.com/course/ingenieria-de-variables-para-machine-learning/?referralCode=CE398C784F17BD87482C)
-
-* [Ingeniería de variables, MachinLenin, charla online](https://www.youtube.com/watch?v=NhCxOOoFXds)
-
-
 ## Documentation
 
 * [Documentation](http://feature-engine.readthedocs.io)
@@ -126,6 +119,7 @@ transforming parameters from the data and then transform it.
  * RecursiveFeatureElimination
  * RecursiveFeatureAddition
  * DropHighPSIFeatures
+ * SelectByInformationValue
 
 ### Datetime
  * DatetimeFeatures
diff --git a/docs/index.rst b/docs/index.rst
index 497ecdbe3..6d4055525 100644
--- a/docs/index.rst
+++ b/docs/index.rst
@@ -46,10 +46,10 @@ What is unique about Feature-engine?
 
 The following characteristics make Feature-engine unique:
 
-- Feature-engine contains the most exhaustive battery of feature engineering transformations.
+- Feature-engine contains the most exhaustive collection of feature engineering transformations.
 - Feature-engine can transform a specific group of variables in the dataframe.
 - Feature-engine returns dataframes, hence suitable for data exploration and model deployment.
-- Feature-engine is compatible with the Scikit-learn pipeline.
+- Feature-engine is compatible with the Scikit-learn pipeline, Grid and Random search and cross validation.
 - Feature-engine automatically recognizes numerical, categorical and datetime variables.
 - Feature-engine alerts you if a transformation is not possible, e.g., if applying logarithm to negative variables or divisions by 0.
 
@@ -96,7 +96,7 @@ Feature-engine features in the following resources
 - `Feature Engineering for Machine Learning <https://courses.trainindata.com/p/feature-engineering-for-machine-learning>`_, Online Course.
 - `Feature Selection for Machine Learning <https://courses.trainindata.com/p/feature-selection-for-machine-learning>`_, Online Course.
 - `Feature Engineering for Time Series Forecasting <https://www.courses.trainindata.com/p/feature-engineering-for-forecasting>`_, Online Course.
-- `Python Feature Engineering Cookbook <https://amzn.to/3TmOL3I>`_, book.
+- `Python Feature Engineering Cookbook <https://packt.link/0ewSo>`_, book.
 - `Feature Selection in Machine Learning with Python <https://leanpub.com/feature-selection-in-machine-learning>`_, book.
 
 More learning resources in the :ref:`**Learning Resources** <learning_resources>`.
diff --git a/docs/resources/books.rst b/docs/resources/books.rst
index 683acce26..39579cdc6 100644
--- a/docs/resources/books.rst
+++ b/docs/resources/books.rst
@@ -8,7 +8,7 @@ in the following books:
    :width: 200
    :figclass: align-center
    :align: left
-   :target: https://amzn.to/3TmOL3I
+   :target: https://packt.link/0ewSo
 
    Python Feature Engineering Cookbook