Fixed some silly bugs in erm-p1

app/posts/erm-p1/page.mdx

@@ -12,7 +12,6 @@ import training_image from "./training.svg";
 
 **Empirical Risk Minimization (ERM)** is a mathematical framework for performing supervised machine learning (ML). This will be **part 1** in a series of blog posts exploring ERM from the ground up. 
 
-
 In **part 1**, we will broadly cover the task of supervised machine learning in a non-rigorous manner. By the end of this post, you should understand what supervised machine learning entails, so that later parts can formalize these concepts using math.
 
 <hr className="mt-3"/>
@@ -43,7 +42,7 @@ An ML researcher may choose to implement a _classification_ model for fraud dete
     - _fraudulent_ - if the model believes the purchase is fraudulent 
     - _not fraudulent_ - otherwise
 
-We'll defer a conversation on _how_ the model predict if a credit card purchase is fraudulent to the [Features and Labels](#features-and-labels) section. 
+We'll defer a conversation on _how_ the model predict if a credit card purchase is fraudulent to the [Training and Learning](#training-and-learning) section. 
 </Example>
 
 ## Features and Labels