diff --git a/doc/src/week38/week38.do.txt b/doc/src/week38/week38.do.txt
index 590a65fa7..ee7d4d4a5 100644
--- a/doc/src/week38/week38.do.txt
+++ b/doc/src/week38/week38.do.txt
@@ -1126,15 +1126,25 @@ f(y_i\vert x_i)=\beta_0+\beta_1 x_i.
 \]
 !et
 
-This expression implies however that $f(y_i\vert x_i)$ could take any value from minus infinity to plus infinity. If we however let $f(y\vert y)$ be represented by the mean value, the above example shows us that we can constain to be between zero and one, that is we have $0 \le f(y_i\vert x_i) \le 1$. Looking at our last curve we see also that it has an S-shaped form. This leads us to a very popular model for the function $f$, namely the so-called Sigmoid function or logistic model. We will consider this function as representing the probability for finding a value of $y_i$ with a given $x_i$. 
+This expression implies however that $f(y_i\vert x_i)$ could take any
+value from minus infinity to plus infinity. If we however let
+$f(y\vert y)$ be represented by the mean value, the above example
+shows us that we can constrain the function to take values between
+zero and one, that is we have $0 \le f(y_i\vert x_i) \le 1$. Looking
+at our last curve we see also that it has an S-shaped form. This leads
+us to a very popular model for the function $f$, namely the so-called
+Sigmoid function or logistic model. We will consider this function as
+representing the probability for finding a value of $y_i$ with a given
+$x_i$.
 
 !split
 ===== The logistic function =====
 
-The perceptron is an example of a ``hard classification'' model. We
+Another widely studied model, is the so-called 
+perceptron model, which is an example of a ``hard classification'' model. We
 will encounter this model when we discuss neural networks as
 well. Each datapoint is deterministically assigned to a category (i.e
-$y_i=0$ or $y_i=1$). In many cases, it is favorable to have a ``soft''
+$y_i=0$ or $y_i=1$). In many cases, and the coronary heart disease data forms one of many such examples, it is favorable to have a ``soft''
 classifier that outputs the probability of a given category rather
 than a single value. For example, given $x_i$, the classifier
 outputs the probability of being in a category $k$.  Logistic regression
@@ -1344,14 +1354,14 @@ p(\hat{\beta}\hat{x})=\frac{ \exp{(\beta_0+\beta_1x_1+\beta_2x_2+\dots+\beta_px_
 
 Till now we have mainly focused on two classes, the so-called binary
 system. Suppose we wish to extend to $K$ classes.  Let us for the sake
-of simplicity assume we have only two predictors. We have then
-following model
+of simplicity assume we have only two predictors. We have then following model
 
 !bt
 \[
 \log{\frac{p(C=1\vert x)}{p(K\vert x)}} = \beta_{10}+\beta_{11}x_1,
 \]
 !et
+and 
 !bt
 \[
 \log{\frac{p(C=2\vert x)}{p(K\vert x)}} = \beta_{20}+\beta_{21}x_1,
@@ -1405,14 +1415,14 @@ descent method.  Newton's method and gradient descent methods are
 discussed in the material on "optimization
 methods":"https://compphysics.github.io/MachineLearning/doc/pub/Splines/html/Splines-bs.html".
 
-This will be discussed next week.
+This will be discussed next week. Before we develop our own codes for logistic regression, we end this lecture by studying the functionality that _Scikit-learn_ offers. 
 
 
 
 
 
 !split
-=====  Cancer Data again now with Decision Trees and other Methods =====
+=====  Wisconsin Cancer Data  =====
 !bc pycod
 import matplotlib.pyplot as plt
 import numpy as np