Toolbox.md

@@ -2,10 +2,14 @@
 
 ## Python for ML applications 
 
+- [Python for beginners](https://www.youtube.com/watch?v=rfscVS0vtbw)
+
 - [Python Like You Mean It](https://www.pythonlikeyoumeanit.com/)
 
 - [Data Preprocessing in Python](https://medium.datadriveninvestor.com/data-preprocessing-3cd01eefd438)
 
+-[The Jupyter Notebook](https://jupyter-notebook.readthedocs.io/en/stable/)
+
 - [Better Heatmaps and Correlation Matrix Plots](https://towardsdatascience.com/better-heatmaps-and-correlation-matrix-plots-in-python-41445d0f2bec)
 
 ## ML libraries and tools
@@ -16,6 +20,27 @@
 - [Facebook field guide to ML](https://research.fb.com/blog/2018/05/the-facebook-field-guide-to-machine-learning-video-series/)
 
 
+## Datasets
+
+Below you can find a list of resources to check for interesting datasets:
+
+- <https://archive.ics.uci.edu>
+
+- <https://dataverse.harvard.edu/>
+
+- <https://paperswithcode.com/>
+
+- <https://datasetsearch.research.google.com/>
+
+- <https://cloud.google.com/bigquery/public-data/>
+
+- <https://registry.opendata.aws/>
+
+- <https://www.kaggle.com/>
+
+- <https://en.wikipedia.org/wiki/List_of_datasets_for_machine-learning_research>
+
+
 ## Scikit learn
 
 We will be using Scikit-Learn for importing a variety of ML libraries, particularly in the first 4 weeks of the lecture. Here you can find additional online resources on the "smart" usage of this practical tool.