Merge pull request #377 from Etrama/intro_lang

01-introdruction.Rmd language fix

manuscript/01-introduction.Rmd

@@ -18,12 +18,12 @@ Internalizing the basic concepts also empowers you to better understand and eval
 
 This book starts with some (dystopian) [short stories](#storytime) that are not needed to understand the book, but hopefully will entertain and make you think.
 Then the book explores the concepts of [machine learning interpretability](#interpretability).
-We will discuss when interpretability is important and what different types of explanations there are.
+We will discuss when interpretability is important and the different types of explanations that exist.
 Terms used throughout the book can be looked up in the [Terminology chapter](#terminology).
 Most of the models and methods explained are presented using real data examples which are described in the [Data chapter](#data).
 One way to make machine learning interpretable is to use [interpretable models](#simple), such as linear models or decision trees.
 The other option is the use of [model-agnostic interpretation tools](#agnostic) that can be applied to any supervised machine learning model.
-Model-agnostic methods can be divided [global methods](#global-methods) that describe the average behavior of the model and [local methods](#local-methods) that explain individual predictions.
+Model-agnostic methods can be divided into [global methods](#global-methods) that describe the average behavior of the model, and [local methods](#local-methods) that explain individual predictions.
 The Model-Agnostic Methods chapter deals with methods such as [partial dependence plots](#pdp) and [feature importance](#feature-importance).
 Model-agnostic methods work by changing the input of the machine learning model and measuring changes in the prediction output.
 The book ends with an optimistic outlook on what [the future of interpretable machine learning](#future) might look like.