diff --git a/content/learning-paths/smartphones-and-mobile/profiling-ml-on-arm/why-profile.md b/content/learning-paths/smartphones-and-mobile/profiling-ml-on-arm/why-profile.md
index b1d4b7035f..ce0a2ea8ed 100644
--- a/content/learning-paths/smartphones-and-mobile/profiling-ml-on-arm/why-profile.md
+++ b/content/learning-paths/smartphones-and-mobile/profiling-ml-on-arm/why-profile.md
@@ -7,7 +7,7 @@ layout: learningpathall
 ---
 
 ## Optimizing Performance
-A first step towards achieving optimal performance in a ML Model is to identify what is consuming the most time and memory in your application. Profiling can help you identify the bottlenecks, and it can offer clues about how to optimize operations.
+A first step towards achieving optimal performance in a Machine Learning Model is to identify what is consuming the most time and memory in your application. Profiling can help you identify the bottlenecks, and it can offer clues about how to optimize operations.
 
 With Machine Learning (ML) applications, whilst the inference of the Neural Network (NN) is often the heaviest part of the application in terms of computation and memory usage, it is not necessarily always the case. It is therefore important to profile the application as a whole to detect other possible issues that can negatively impact performance, such as issues with pre- or post-processing, or the code itself.