diff --git a/4-Classification/3-Classifiers-2/README.md b/4-Classification/3-Classifiers-2/README.md
index e5b7229c..f63bbbfd 100644
--- a/4-Classification/3-Classifiers-2/README.md
+++ b/4-Classification/3-Classifiers-2/README.md
@@ -85,12 +85,12 @@ Accuracy (train) for Linear SVC: 78.6%
 weighted avg       0.79      0.79      0.79      1199
 ```
 
-✅ Learn about linear SVC
+✅ Learn about Linear SVC
 
 Support-Vector clustering (SVC) is a child of the Support-Vector machines family of ML techniques (learn more about these below). In this method, you can choose a 'kernel' to decide how to cluster the labels. The 'C' parameter refers to 'regularization' which regulates the influence of parameters. The kernel can be one of [several](https://scikit-learn.org/stable/modules/generated/sklearn.svm.SVC.html#sklearn.svm.SVC); here we set it to 'linear' to ensure that we leverage linear SVC. Probability defaults to 'false'; here we set it to 'true' to gather probability estimates. We set the random state to '0' to shuffle the data to get probabilities.
 ## K-Neighbors classifier
 
-The previous classifier was good, and worked well with the data, but maybe we can get better accuracy. Try a K-Neighbors classifer. Add a line to your classifier array (add a comma after the Linear SVC item):
+The previous classifier was good, and worked well with the data, but maybe we can get better accuracy. Try a K-Neighbors classifier. Add a line to your classifier array (add a comma after the Linear SVC item):
 
 ```python
 'KNN classifier': KNeighborsClassifier(C),
diff --git a/quiz-app/src/assets/translations/en.json b/quiz-app/src/assets/translations/en.json
index c390eb50..077a99bd 100644
--- a/quiz-app/src/assets/translations/en.json
+++ b/quiz-app/src/assets/translations/en.json
@@ -1251,49 +1251,53 @@
 				"title": "Classification 3: Pre-Lecture Quiz",
 				"quiz": [
 					{
-						"questionText": "q1",
+						"questionText": "A good initial classifier to try is:",
 						"answerOptions": [
 							{
-								"answerText": "a",
-								"isCorrect": "false"
+								"answerText": "Linear SVC",
+								"isCorrect": "true"
 							},
 							{
-								"answerText": "b",
-								"isCorrect": "true"
+								"answerText": "K-Means",
+								"isCorrect": "false"
 							},
 							{
-								"answerText": "c",
+								"answerText": "Logical SVC",
 								"isCorrect": "false"
 							}
 						]
 					},
 					{
-						"questionText": "q2",
+						"questionText": "Regularization controls:",
 						"answerOptions": [
 							{
-								"answerText": "a",
+								"answerText": "the influence of parameters",
 								"isCorrect": "true"
 							},
 							{
-								"answerText": "b",
+								"answerText": "the influence of training speed",
+								"isCorrect": "false"
+							},
+							{
+								"answerText": "the influence of outliers",
 								"isCorrect": "false"
 							}
 						]
 					},
 					{
-						"questionText": "q3",
+						"questionText": "K-Neighbors classifier can be used for:",
 						"answerOptions": [
 							{
-								"answerText": "a",
+								"answerText": "supervised learning",
 								"isCorrect": "false"
 							},
 							{
-								"answerText": "b",
-								"isCorrect": "true"
+								"answerText": "unsupervised learning",
+								"isCorrect": "false"
 							},
 							{
-								"answerText": "c",
-								"isCorrect": "false"
+								"answerText": "both of these",
+								"isCorrect": "true"
 							}
 						]
 					}
@@ -1304,48 +1308,52 @@
 				"title": "Classification 3: Post-Lecture Quiz",
 				"quiz": [
 					{
-						"questionText": "q1",
+						"questionText": "Support-Vector classifiers can be used for",
 						"answerOptions": [
 							{
-								"answerText": "a",
+								"answerText": "classification",
 								"isCorrect": "false"
 							},
 							{
-								"answerText": "b",
-								"isCorrect": "true"
+								"answerText": "regression",
+								"isCorrect": "false"
 							},
 							{
-								"answerText": "c",
-								"isCorrect": "false"
+								"answerText": "both of these",
+								"isCorrect": "true"
 							}
 						]
 					},
 					{
-						"questionText": "q2",
+						"questionText": "Random Forest is a ___ type of classifier",
 						"answerOptions": [
 							{
-								"answerText": "a",
+								"answerText": "Ensemble",
 								"isCorrect": "true"
 							},
 							{
-								"answerText": "b",
+								"answerText": "Dissemble",
+								"isCorrect": "false"
+							},
+							{
+								"answerText": "Assemble",
 								"isCorrect": "false"
 							}
 						]
 					},
 					{
-						"questionText": "q3",
+						"questionText": "Adaboost is known for:",
 						"answerOptions": [
 							{
-								"answerText": "a",
-								"isCorrect": "false"
+								"answerText": "focusing on the weights of incorrectly classified items",
+								"isCorrect": "true"
 							},
 							{
-								"answerText": "b",
-								"isCorrect": "true"
+								"answerText": "focusing on outliers",
+								"isCorrect": "false"
 							},
 							{
-								"answerText": "c",
+								"answerText": "focusing on incorrect data",
 								"isCorrect": "false"
 							}
 						]