diff --git a/docs/build_docs.py b/docs/build_docs.py
index f685fddd..1744aeff 100644
--- a/docs/build_docs.py
+++ b/docs/build_docs.py
@@ -199,11 +199,12 @@ def convert_plaintext_links_to_html(content):
         for text_node in paragraph.find_all(string=True, recursive=False):
             if text_node.parent.name not in {"a", "code"}:  # Ignore links and code blocks
                 new_text = re.sub(
-                    r"(https?://[^\s()<>]*[^\s()<>.,:;!?])",
+                    r"(https?://[^\s()<>]*[^\s()<>.,:;!?\'\"])",
                     r'<a href="\1">\1</a>',
                     str(text_node),
                 )
                 if "<a href=" in new_text:
+                    # Parse the new text with BeautifulSoup to handle HTML properly
                     new_soup = BeautifulSoup(new_text, "html.parser")
                     text_node.replace_with(new_soup)
                     modified = True
diff --git a/docs/en/datasets/classify/mnist.md b/docs/en/datasets/classify/mnist.md
index 055c8b77..356fdc4f 100644
--- a/docs/en/datasets/classify/mnist.md
+++ b/docs/en/datasets/classify/mnist.md
@@ -6,7 +6,7 @@ keywords: MNIST, dataset, handwritten digits, image classification, deep learnin
 
 # MNIST Dataset
 
-The [MNIST](http://yann.lecun.com/exdb/mnist/) (Modified National Institute of Standards and Technology) dataset is a large database of handwritten digits that is commonly used for training various image processing systems and machine learning models. It was created by "re-mixing" the samples from NIST's original datasets and has become a benchmark for evaluating the performance of image classification algorithms.
+The [MNIST](https://en.wikipedia.org/wiki/MNIST_database) (Modified National Institute of Standards and Technology) dataset is a large database of handwritten digits that is commonly used for training various image processing systems and machine learning models. It was created by "re-mixing" the samples from NIST's original datasets and has become a benchmark for evaluating the performance of image classification algorithms.
 
 ## Key Features
 
@@ -83,13 +83,13 @@ research or development work, please cite the following paper:
         }
         ```
 
-We would like to acknowledge Yann LeCun, Corinna Cortes, and Christopher J.C. Burges for creating and maintaining the MNIST dataset as a valuable resource for the [machine learning](https://www.ultralytics.com/glossary/machine-learning-ml) and [computer vision](https://www.ultralytics.com/glossary/computer-vision-cv) research community. For more information about the MNIST dataset and its creators, visit the [MNIST dataset website](http://yann.lecun.com/exdb/mnist/).
+We would like to acknowledge Yann LeCun, Corinna Cortes, and Christopher J.C. Burges for creating and maintaining the MNIST dataset as a valuable resource for the [machine learning](https://www.ultralytics.com/glossary/machine-learning-ml) and [computer vision](https://www.ultralytics.com/glossary/computer-vision-cv) research community. For more information about the MNIST dataset and its creators, visit the [MNIST dataset website](https://en.wikipedia.org/wiki/MNIST_database).
 
 ## FAQ
 
 ### What is the MNIST dataset, and why is it important in machine learning?
 
-The [MNIST](http://yann.lecun.com/exdb/mnist/) dataset, or Modified National Institute of Standards and Technology dataset, is a widely-used collection of handwritten digits designed for training and testing image classification systems. It includes 60,000 training images and 10,000 testing images, all of which are grayscale and 28x28 pixels in size. The dataset's importance lies in its role as a standard benchmark for evaluating image classification algorithms, helping researchers and engineers to compare methods and track progress in the field.
+The [MNIST](https://en.wikipedia.org/wiki/MNIST_database) dataset, or Modified National Institute of Standards and Technology dataset, is a widely-used collection of handwritten digits designed for training and testing image classification systems. It includes 60,000 training images and 10,000 testing images, all of which are grayscale and 28x28 pixels in size. The dataset's importance lies in its role as a standard benchmark for evaluating image classification algorithms, helping researchers and engineers to compare methods and track progress in the field.
 
 ### How can I use Ultralytics YOLO to train a model on the MNIST dataset?