diff --git a/lessons/4-ComputerVision/12-Segmentation/README.md b/lessons/4-ComputerVision/12-Segmentation/README.md
index e2250b67..d2eb43f0 100644
--- a/lessons/4-ComputerVision/12-Segmentation/README.md
+++ b/lessons/4-ComputerVision/12-Segmentation/README.md
@@ -63,5 +63,4 @@ This [wikipedia article](https://wikipedia.org/wiki/Image_segmentation) offers a
 
 ## [Assignment](lab/README.md)
 
-In this lab, try **human body segmentation** using [Segmentation Full Body MADS Dataset](https://www.kaggle.com/datasets/tapakah68/segmentation-full-body-mads-dataset) from Kaggle.
-
+In this lab, try **human body segmentation** using [Segmentation Full Body MADS Dataset](https://www.kaggle.com/datasets/tapakah68/segmentation-full-body-mads-dataset) from Kaggle.
\ No newline at end of file
diff --git a/lessons/5-NLP/README.md b/lessons/5-NLP/README.md
index 09262e5b..7379f3f7 100644
--- a/lessons/5-NLP/README.md
+++ b/lessons/5-NLP/README.md
@@ -3,7 +3,7 @@
 
 In this section, we will focus on using Neural Networks to handle tasks related to natural language processing (NLP). There are many NLP problems that we want computers to be able to solve:
 
-* **Text classification** is typical classification problem on text sequences. Examples include classifying e-mail messages on spam vs. no-spam, or attributing news article into one of the pre-defined categories (sport, business, politics, etc.). Also, when developing chat bots, we often need to understand what a used wanted to say -- in this case we are dealing with **intent classificaton**. Often, in intent classification we need to deal with many categories.
+* **Text classification** is a typical classification problem pertaining to text sequences. Examples include classifying e-mail messages on spam vs. no-spam, or categorizing news article as sport, business, politics, etc. Also, when developing chat bots, we often need to understand what a user wanted to say -- in this case we are dealing with **intent classificaton**. Often, in intent classification we need to deal with many categories.
 * **Sentiment analysis** is typical regression problem, where we need to attribute a number -- sentiment -- corresponding to how positive/negative the meaning of a sentence is. More advanced version of sentiment analysis is **aspect-based sentiment analysis** (ABSA), where we attribute sentiment not the the whole sentence, but to different parts of it (aspects), eg. *In this restaurant, I liked the cuisine, but the atmosphere was awful*.
 * **Named Entity Recognition** (NER) refers to the problem of extracting certain entities from text. For example, we might need to understand that in the phrase *I need to fly to Paris tomorrow* the word *tomorrow* refers to DATE, and *Paris* is a LOCATION.  
 * **Keyword extraction** is similar to NER, but we need to extract words important to the meaning of the sentence automatically, without pre-training for specific entity types.
@@ -48,7 +48,8 @@ if len(physical_devices)>0:
     tf.config.experimental.set_memory_growth(physical_devices[0], True) 
 ```
 
-## Contents
+## In this Section
+In this section we will learn about:
 
 * [Representing text as tensors](13-TextRep/README.md)
 * [Word Embeddings](14-Emdeddings/README.md)