get-started: agenda copy edits

static/docs/get-started/agenda.md

@@ -1,36 +1,35 @@
 # Agenda
 
-In the next few sections we will build a simple natural language processing
-(NLP) project from scratch. If you'd like to get the final result or have some
-issues along the way, you can download the fully reproducible
-[GitHub project](https://github.com/iterative/example-get-started) with:
+In the next few sections we'll build a simple natural language processing (NLP)
+project from scratch. If you'd like to get the final result or have any issues
+along the way, you can download the fully reproducible
+[GitHub project](https://github.com/iterative/example-get-started) by running:
 
 ```dvc
 $ git clone https://github.com/iterative/example-get-started
 ```
 
-Otherwise, bear with us and we will introduce the basic DVC concepts and get to
-the same result together!
+Otherwise, bear with us and we'll introduce some basic DVC concepts to get the
+same results together!
 
-The idea of the project is a simplified version of the
-[Tutorial](/doc/tutorials/deep). It explores the NLP problem of predicting tags
-for a given StackOverflow question. For example, we want a classifier that can
-predict posts about the Python language by tagging them `python`.
+The idea of the project is a simplified version of our
+[Deep Dive Tutorial](/doc/tutorials/deep). It explores the NLP problem of
+predicting tags for a given StackOverflow question. For example, we might want a
+classifier that can classify (or predict) posts about Python by tagging them
+with `python`.
 
 ![](/static/img/example-flow-2x.png)
 
-Do not let the NLP nature of the example discourage you from using DVC in other
-Data Science areas. There was no strong reason behind picking the NLP area. On
-contrary, DVC is designed to be agnostic of frameworks, languages, etc. If you
-have data files or datasets and/or you produce other data files, models,
-datasets and you want to:
+This is a natural language processing context, but NLP isn't the only area of
+data science where DVC can help. DVC is designed to be agnostic of frameworks,
+languages, etc. If you have data files or datasets and/or you produce data
+files, models, or datasets and you want to:
 
-- Capture and save those <abbr>data artifacts</abbr> the same way we capture
+- Capture and save those <abbr>data artifacts</abbr> the same way you capture
   code
-- Track and switch between different versions of the data easily
-- Be able to answer the question of how data artifacts (e.g. ML models) were
-  built in the first place
-- Be able to compare them
-- Bring best practices to your team and get everyone on the same page
+- Track and switch between different versions of data easily
+- Understand how data artifacts (e.g. ML models) were built in the first place
+- Be able to compare models to each other
+- Bring software best practices to your team and get everyone on the same page
 
-Then you are in a good place! Click the `Next` button below to start ↘
+Then you're in the right place! Click the `Next` button below to start ↘