diff --git a/Part 1 - Introduction to Machine Learning with scikit-learn.md b/Part 1 - Introduction to Machine Learning with scikit-learn.md
index 6903a57..480830d 100644
--- a/Part 1 - Introduction to Machine Learning with scikit-learn.md	
+++ b/Part 1 - Introduction to Machine Learning with scikit-learn.md	
@@ -1,7 +1,9 @@
 # Finding a Dataset
 The first step to developing a good machine learning algorithm is using a good dataset. Many of the most accurate machine learning algorithms have millions if not billions of entries in their training data sets. Fortuntately for us, there are many small yet robust datasets we can use to build our ML algorithms. 
 
-The scikit-learn library comes with some good starting datasets. For today's activity, we'll be recognizing handwritten numbers from scikit-learn's `digits` dataset. To use this dataset, we'll import the `load_digits` function from `sklearn.datasets` and store it in a variable called `digits`.
+The scikit-learn library comes with some good starting datasets. For today's activity, we'll be recognizing handwritten numbers from scikit-learn's `digits` dataset. This dataset contains over 1700 labeled 8x8 pixel images of handrawn numerical digits.
+
+To use this dataset, we'll import the `load_digits` function from `sklearn.datasets` and store it in a variable called `digits`.
 ```
 from sklearn.datasets import load_digits
 digits = load_digits()
@@ -125,9 +127,11 @@ Logistic Regression accuracy: 94.88320355951056%
 ```
  
 
-Clearly, logistic regression is a far more suitable algorithm for correctly determining a handwritten number - it achieves a 94.88% accuracy while linear regression is hardly better than a coinflip!
+Clearly, logistic regression is a far more suitable algorithm for correctly determining a handwritten number - it achieves a 94.88% accuracy while linear regression is hardly better than a coinflip! But can we do better? 
+
+**Answer:** Yes, with a neural network. 
 
 
 ### 📚 Further Reading
-For an exhaustive list of the machine learning algorithms scikit-learn has to offer, check out [this page in their documentation] (https://scikit-learn.org/stable/supervised_learning.html).
+For an exhaustive list of the machine learning algorithms scikit-learn has to offer, check out [this page in their documentation] (https://scikit-learn.org/stable/supervised_learning.html). Machine learning algorithms are not one size fits all - different problems require different algorithms. There are many cases where linear regression will outperform logistic regression, for instance, so it's good to understand the various types of machine learning algorithms.