update

Author: spsanderson

README.Rmd

@@ -25,8 +25,9 @@ knitr::opts_chunk$set(
 
 ## Introduction
 
-The goal of the `{tidyaml}` package is to serve as a sort of __Auto ML__ for the
-__`tidymodels`__ ecosystem. Some ideas are that we should be able to generate regression
+Welcome to __`{tidyaml}`__ which is a new R package that makes it easy to use the `tidymodels` ecosystem to perform automated machine learning (AutoML). This package provides a simple and intuitive interface that allows users to quickly generate machine learning models without worrying about the underlying details. It also includes a safety mechanism that ensures that the package will fail gracefully if any required extension packages are not installed on the user's machine. With `{tidyaml}`, users can easily build high-quality machine learning models in just a few lines of code. Whether you are a beginner or an experienced machine learning practitioner, `{tidyaml}` has something to offer.
+
+Some ideas are that we should be able to generate regression
 models on the fly without having to actually go through the process of building the
 specification, especially if it is a non-tuning model, meaning we are not planing
 on tuning hyper-parameters like `penalty` and `cost`.

README.md

@@ -15,12 +15,22 @@ Welcome](https://img.shields.io/badge/PRs-welcome-brightgreen.svg?style=flat-squ
 
 ## Introduction
 
-The goal of the `{tidyaml}` package is to serve as a sort of **Auto ML**
-for the **`tidymodels`** ecosystem. Some ideas are that we should be
-able to generate regression models on the fly without having to actually
-go through the process of building the specification, especially if it
-is a non-tuning model, meaning we are not planing on tuning
-hyper-parameters like `penalty` and `cost`.
+Welcome to **`{tidyaml}`** which is a new R package that makes it easy
+to use the `tidymodels` ecosystem to perform automated machine learning
+(AutoML). This package provides a simple and intuitive interface that
+allows users to quickly generate machine learning models without
+worrying about the underlying details. It also includes a safety
+mechanism that ensures that the package will fail gracefully if any
+required extension packages are not installed on the user’s machine.
+With `{tidyaml}`, users can easily build high-quality machine learning
+models in just a few lines of code. Whether you are a beginner or an
+experienced machine learning practitioner, `{tidyaml}` has something to
+offer.
+
+Some ideas are that we should be able to generate regression models on
+the fly without having to actually go through the process of building
+the specification, especially if it is a non-tuning model, meaning we
+are not planing on tuning hyper-parameters like `penalty` and `cost`.
 
 The idea is not to re-write the excellent work the `tidymodels` team has
 done (because it’s not possible) but rather to try and make an enhanced