editorial tweaking

4-Classification/2-Classifiers-1/README.md

@@ -120,13 +120,13 @@ Let's see if we can reason our way through different approaches given the constr
 - **Neural networks are too heavy**. Given our clean, but minimal dataset, and the fact that we are running training locally via notebooks, neural networks are too heavyweight for this task.
 - **No two-class classifier**. We do not use a two-class classifier, so that rules out one-vs-all. 
 - **Decision tree or logistic regression could work**. A decision tree might work, or logistic regression for multiclass data. 
-- **Multiclass , wrong fit**. The multiclass boosted decision tree is most suitable for nonparametric tasks, e.g. tasks designed to build rankings, so it is not useful for us.
+- **Multiclass Boosted Decision Trees solve a different problem**. The multiclass boosted decision tree is most suitable for nonparametric tasks, e.g. tasks designed to build rankings, so it is not useful for us.
 
-### Using Scikit 
+### Using Scikit-learn 
 
-We will be using SciKit to analyze our data. However, there are many ways to use Logistic Regression in Scikit-learn. Take a look at the [parameters to pass](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html?highlight=logistic%20regressio#sklearn.linear_model.LogisticRegression).  
+We will be using Scikit-learn to analyze our data. However, there are many ways to use logistic regression in Scikit-learn. Take a look at the [parameters to pass](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html?highlight=logistic%20regressio#sklearn.linear_model.LogisticRegression).  
 
-Essentially there are two important parameters `multi_class` and `solver`, that we need to specify, when we ask SciKit to perform a Logistic Regression. The `multi_class` value applies a certain behavior. The value of the solver is what algorithm to use. Not all solvers can be paired with all `multi_class` values.
+Essentially there are two important parameters `multi_class` and `solver`, that we need to specify, when we ask Scikit-learn to perform a logistic regression. The `multi_class` value applies a certain behavior. The value of the solver is what algorithm to use. Not all solvers can be paired with all `multi_class` values.
 
 According to the docs, in the multiclass case, the training algorithm:
 

4-Classification/3-Classifiers-2/README.md

@@ -1,6 +1,6 @@
 # Cuisine classifiers 2
 
-In this second classification lesson, you will explore more ways to classify numeric data. You will also learn about the ramifications for choosing one over the other.
+In this second classification lesson, you will explore more ways to classify numeric data. You will also learn about the ramifications for choosing one classifier over the other.
 
 ## [Pre-lecture quiz](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/23/)
 

6-NLP/4-Hotel-Reviews-1/README.md

@@ -1,4 +1,4 @@
-# Sentiment analysis with hotel reviews - cleaning the data
+# Sentiment analysis with hotel reviews - processing the data
 
 In this section you will use the techniques in the previous lessons to do some exploratory data analysis of a large dataset. Once you have a good understanding of the usefulness of the various columns, you will learn how to remove the unneeded columns, calculate some new data based on the existing columns, and save the resulting dataset for use in the final challenge.