Some more copy edits.

EuroScipy.ipynb

@@ -846,9 +846,9 @@
    "source": [
     "### 5.3 Logistic Regression\n",
     "\n",
-    "Logistic regression predicts the probability that a sample belongs to a class based on the values of the input variables, based on a linear model. In the case of classification, we can use this to then assign the sample to the most likely class.\n",
+    "Logistic regression is based on linear regression, but rather than the predicted output being a continuous value, it predicts the probability that a sample belongs to a class based on the values of the input variables (for more details, see: http://www.omidrouhani.com/research/logisticregression/html/logisticregression.htm). In the case of classification, we can use this to then assign the sample to the most likely class.\n",
     "\n",
-    "Building a logistic regression model with default parameters .. blah blah.."
+    "In sklearn, you can learn a logistic regression model using the LogisticRegression object (http://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html). As with linear regression, there are certain assumpttions that you might make or constraints that you wish your model to fulfil, e.g. whether or not you want a constant to be included in the function. You can also specify the way you wish learning to take place by using different solvers or how you wish errors to be penalised."
    ]
   },
   {