Updated README.org

README.org

@@ -24,32 +24,35 @@ contributing!*
 So far, notebooks are listed alphabetically and stars shall indicate
 their difficulty (☆ = simple, ☆☆ = advanced, ☆☆☆ = sophisticated):
 
+
+- [[file:classification.ipynb][Classification]] :: basic machine learning classification example (☆) 
+- [[file:exponential_smoothing.ipynb][Exponential smoothing]] :: using [[https://ipywidgets.readthedocs.io/en/latest/examples/Widget%2520Basics.html][Jupyter's interactive widget]] to
+     explore [[https://en.wikipedia.org/wiki/Exponential_smoothing][exponential smoothing]] (☆)
+- [[file:Hamming.ipynb][Hamming]] :: a graph visualising a strange type of word similarity (☆)
+- [[file:Jupyter-Demo.ipynb][Jupyter-Demo]] :: demo of some Jupyter features useful for creating
+     learning material (☆)
+- [[file:Twitter.ipynb][Twitter]] :: analysing Twitter data (raw JSON from Twitter's API) (☆)
+- [[file:wikipedia_language_editions.ipynb][Wikipedia language editions]] :: plotting the depth and number of articles of different
+    Wikipedia language editions (☆)
 - [[file:AirBnB_Use_Berlin.ipynb][AirBnB Use in Berlin]] :: exemplary (and excellent) term paper for the
      module "Datenanalyse & -auswertung" (☆☆)
 - [[file:amazon_reviews.ipynb][Amazon reviews]] :: crawling web sites with [[https://scrapy.org/][Scrapy]], processing JSON
      data, basic statistics and visualisation (☆☆)
-- [[file:Art.ipynb][Computer-generated art]] :: translation, scaling and composition of
+- [[file:Art.ipynb][Art]] :: Creating computer-generated art by translation, scaling and composition of
   functions (☆☆)
-- [[file:classification.ipynb][Classification]] :: basic machine learning classification example (☆)
 - [[file:community_detection.ipynb][Community detection]] :: applying community detection algorithms to
      network graphs (☆☆)
 - [[file:crawling_a_blog.ipynb][Crawling a blog]] :: crawling web sites, basic text mining, basic
      statistics and visualisation (☆☆)
-- [[file:distances.ipynb][Distances]] :: comprehensive interactive simulation of recovering
-     information from noisy data (namely, point positions given their
-     noisy distance matrix) (☆☆☆)
 - [[file:Dracor.ipynb][DraCor]] :: retrieving data from a REST API, text transformation and
      classification (☆☆)
-- [[file:exponential_smoothing.ipynb][Exponential smoothing]] :: using [[https://ipywidgets.readthedocs.io/en/latest/examples/Widget%2520Basics.html][Jupyter's interactive widget]] to
-     explore [[https://en.wikipedia.org/wiki/Exponential_smoothing][exponential smoothing]] (☆)
-- [[file:Hamming.ipynb][Hamming]] :: a graph visualising a strange type of word similarity (☆)
-- [[file:Jupyter-Demo.ipynb][Jupyter demo]] :: demo of some Jupyter features useful for creating
-     learning material (☆)
+- Machine Learning :: recipes for common machine learning tasks (☆☆)
 - [[file:Mondrian.ipynb][Mondrian]] :: turtle graphics, recursion, art (☆☆)
-- [[file:Das_Haus_vom_Nikolaus.ipynb][Nikolaus]] :: graph [[https://en.wikipedia.org/wiki/Graph_traversal][traversal]] and drawing (☆☆☆)
 - [[file:statistics_top50faculty.ipynb][Statistics top 50 faculty]] :: exploratory statistical analysis of the
      [[http://cs.brown.edu/people/apapouts/faculty_dataset.html][dataset of 2200 faculty in 50 top US computer science graduate
      programs]] (☆☆)
-- [[file:Twitter.ipynb][Twitter]] :: analysing Twitter data (raw JSON from Twitter's API) (☆)
-- [[file:wikipedia_language_editions.ipynb][Wikipedia]] :: plotting the depth and number of articles of different
-  Wikipedia language editions (☆)
+- [[file:distances.ipynb][Distances]] :: comprehensive interactive simulation of recovering
+     information from noisy data (namely, point positions given their
+     noisy distance matrix) (☆☆☆)
+- [[file:Das_Haus_vom_Nikolaus.ipynb][Das Haus vom Nikolaus]] :: graph [[https://en.wikipedia.org/wiki/Graph_traversal][traversal]] and drawing (☆☆☆)
+- Scrape review blog :: Here, we use the python package scrapy to download all reviews of a literature blog