DOC Added links and corrected typos to plot_stock_market.py
@@ -19,8 +19,9 @@ that are linked tend to fluctuate in relation to each other during a day.
#
# The data is from 2003 - 2008. This is reasonably calm: (not too long ago so
# that we get high-tech firms, and before the 2008 crash). This kind of
-# historical data can be obtained from APIs like the quandl.com and
-# alphavantage.co .
+# historical data can be obtained from APIs like the
+# `data.nasdaq.com <https://data.nasdaq.com/>`_ and
+# `alphavantage.co <https://www.alphavantage.co/>`_.
import sys
import numpy as np
@@ -112,8 +113,8 @@ variation = close_prices - open_prices
#
# We use sparse inverse covariance estimation to find which quotes are
# correlated conditionally on the others. Specifically, sparse inverse
-# covariance gives us a graph, that is a list of connection. For each
-# symbol, the symbols that it is connected too are those useful to explain
+# covariance gives us a graph, that is a list of connections. For each
+# symbol, the symbols that it is connected to are those useful to explain
# its fluctuations.
from sklearn import covariance
