diff --git a/section-04-research-and-development/01-machine-learning-pipeline-data-analysis.ipynb b/section-04-research-and-development/01-machine-learning-pipeline-data-analysis.ipynb
index e17ad470b..df3c3c9f1 100644
--- a/section-04-research-and-development/01-machine-learning-pipeline-data-analysis.ipynb
+++ b/section-04-research-and-development/01-machine-learning-pipeline-data-analysis.ipynb
@@ -4611,8 +4611,8 @@
    "source": [
     "# Additional Resources\n",
     "\n",
-    "- [Feature Engineering for Machine Learning](https://www.udemy.com/course/feature-engineering-for-machine-learning/?referralCode=A855148E05283015CF06) - Online Course\n",
-    "- [Packt Feature Engineering Cookbook](https://www.packtpub.com/data/python-feature-engineering-cookbook) - Book\n",
+    "- [Feature Engineering for Machine Learning](https://www.trainindata.com/p/feature-engineering-for-machine-learning) - Online Course\n",
+    "- [Packt Feature Engineering Cookbook](https://www.amazon.com/Python-Feature-Engineering-Cookbook-transforming-dp-1804611301/dp/1804611301) - Book\n",
     "- [Predict house price with Feature-engine](https://www.kaggle.com/solegalli/predict-house-price-with-feature-engine) - Kaggle kernel\n",
     "- [Comprehensive data exploration with Python](https://www.kaggle.com/pmarcelino/comprehensive-data-exploration-with-python) - Kaggle kernel\n",
     "- [How I made top 0.3% on a Kaggle competition](https://www.kaggle.com/lavanyashukla01/how-i-made-top-0-3-on-a-kaggle-competition) - Kaggle kernel"
diff --git a/section-04-research-and-development/02-machine-learning-pipeline-feature-engineering.ipynb b/section-04-research-and-development/02-machine-learning-pipeline-feature-engineering.ipynb
index e8154e762..f777a746d 100644
--- a/section-04-research-and-development/02-machine-learning-pipeline-feature-engineering.ipynb
+++ b/section-04-research-and-development/02-machine-learning-pipeline-feature-engineering.ipynb
@@ -1652,7 +1652,7 @@
     "\n",
     "For the remaining categorical variables, we will group those categories that are present in less than 1% of the observations. That is, all values of categorical variables that are shared by less than 1% of houses, well be replaced by the string \"Rare\".\n",
     "\n",
-    "To learn more about how to handle categorical variables visit our course [Feature Engineering for Machine Learning](https://www.udemy.com/course/feature-engineering-for-machine-learning/?referralCode=A855148E05283015CF06) in Udemy."
+    "To learn more about how to handle categorical variables visit our course [Feature Engineering for Machine Learning](https://www.trainindata.com/p/feature-engineering-for-machine-learning)."
    ]
   },
   {
@@ -1805,7 +1805,7 @@
     "\n",
     "We will do it so that we capture the monotonic relationship between the label and the target.\n",
     "\n",
-    "To learn more about how to encode categorical variables visit our course [Feature Engineering for Machine Learning](https://www.udemy.com/course/feature-engineering-for-machine-learning/?referralCode=A855148E05283015CF06) in Udemy."
+    "To learn more about how to encode categorical variables visit our course [Feature Engineering for Machine Learning](https://www.trainindata.com/p/feature-engineering-for-machine-learning)."
    ]
   },
   {
@@ -3083,9 +3083,9 @@
     "\n",
     "# Additional Resources\n",
     "\n",
-    "- [Feature Engineering for Machine Learning](https://www.udemy.com/course/feature-engineering-for-machine-learning/?referralCode=A855148E05283015CF06) - Online Course\n",
-    "- [Packt Feature Engineering Cookbook](https://www.packtpub.com/data/python-feature-engineering-cookbook) - Book\n",
-    "- [Feature Engineering for Machine Learning: A comprehensive Overview](https://trainindata.medium.com/feature-engineering-for-machine-learning-a-comprehensive-overview-a7ad04c896f8) - Article\n",
+    "- [Feature Engineering for Machine Learning](https://www.trainindata.com/p/feature-engineering-for-machine-learning) - Online Course\n",
+    "- [Packt Feature Engineering Cookbook](https://www.amazon.com/Python-Feature-Engineering-Cookbook-transforming-dp-1804611301/dp/1804611301) - Book\n",
+    "- [Feature Engineering for Machine Learning: A comprehensive Overview](https://www.blog.trainindata.com/feature-engineering-for-machine-learning/) - Article\n",
     "- [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) - Article"
    ]
   },
diff --git a/section-04-research-and-development/03-machine-learning-pipeline-feature-selection.ipynb b/section-04-research-and-development/03-machine-learning-pipeline-feature-selection.ipynb
index 6d6ef8d9e..c29935ae2 100644
--- a/section-04-research-and-development/03-machine-learning-pipeline-feature-selection.ipynb
+++ b/section-04-research-and-development/03-machine-learning-pipeline-feature-selection.ipynb
@@ -937,9 +937,17 @@
    "source": [
     "# Additional Resources\n",
     "\n",
-    "- [Feature Selection for Machine Learning](https://www.udemy.com/course/feature-selection-for-machine-learning/?referralCode=186501DF5D93F48C4F71) - Online Course\n",
-    "- [Feature Selection for Machine Learning: A comprehensive Overview](https://trainindata.medium.com/feature-selection-for-machine-learning-a-comprehensive-overview-bd571db5dd2d) - Article"
+    "- [Feature Selection for Machine Learning](https://www.trainindata.com/p/feature-selection-for-machine-learning) - Online Course\n",
+    "- [Feature Selection in Machine Learning with Python](https://leanpub.com/feature-selection-in-machine-learning/) - Book\n",
+    "- [Feature Selection for Machine Learning: A comprehensive Overview](https://www.blog.trainindata.com/feature-selection-for-machine-learning/) - Article"
    ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
   }
  ],
  "metadata": {
diff --git a/section-04-research-and-development/05-machine-learning-pPipeline-scoring-new-data.ipynb b/section-04-research-and-development/05-machine-learning-pipeline-scoring-new-data.ipynb
similarity index 100%
rename from section-04-research-and-development/05-machine-learning-pPipeline-scoring-new-data.ipynb
rename to section-04-research-and-development/05-machine-learning-pipeline-scoring-new-data.ipynb
diff --git a/section-04-research-and-development/06-feature-engineering-with-open-source.ipynb b/section-04-research-and-development/06-feature-engineering-with-open-source.ipynb
index 8c6435004..2d25751b3 100644
--- a/section-04-research-and-development/06-feature-engineering-with-open-source.ipynb
+++ b/section-04-research-and-development/06-feature-engineering-with-open-source.ipynb
@@ -2019,7 +2019,7 @@
     "\n",
     "We will do it so that we capture the monotonic relationship between the label and the target.\n",
     "\n",
-    "To learn more about how to encode categorical variables visit our course [Feature Engineering for Machine Learning](https://www.udemy.com/course/feature-engineering-for-machine-learning/?referralCode=A855148E05283015CF06) in Udemy."
+    "To learn more about how to encode categorical variables visit our course [Feature Engineering for Machine Learning](https://www.trainindata.com/p/feature-engineering-for-machine-learning)."
    ]
   },
   {
diff --git a/section-04-research-and-development/08-final-machine-learning-pipeline.ipynb b/section-04-research-and-development/08-final-machine-learning-pipeline.ipynb
index aeaad1589..b514e7786 100644
--- a/section-04-research-and-development/08-final-machine-learning-pipeline.ipynb
+++ b/section-04-research-and-development/08-final-machine-learning-pipeline.ipynb
@@ -1081,7 +1081,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "Python 3 (ipykernel)",
    "language": "python",
    "name": "python3"
   },
@@ -1095,7 +1095,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.8.5"
+   "version": "3.10.5"
   },
   "toc": {
    "base_numbering": 1,