What should the data scientist do to meet these requirements?
Use the Amazon Comprehend entity recognition API operations. Remove the detected words from the blog post data. Replace the blog post data source in the S3 bucket.
Run the SageMaker built-in principal component analysis (PCA) algorithm with the blog post data from the S3 bucket as the data source. Replace the blog post data in the S3 bucket with the results of the training job.
Use the SageMaker built-in Object Detection algorithm instead of the NTM algorithm for the training job to process the blog post data.
Remove the stopwords from the blog post data by using the CountVectorizer function in the scikit-learn library. Replace the blog post data in the S3 bucket with the results of the vectorizer.
Explanations:
The Amazon Comprehend entity recognition API is designed for entity extraction, not for filtering out stopwords. Removing stopwords is better handled during data preprocessing rather than by removing detected entities.
The PCA algorithm is used for dimensionality reduction, not for stopword removal or content filtering in text data. PCA does not address the issue of stopword inclusion in the tags.
The Object Detection algorithm is designed for image processing, not for text data like blog posts. This would not be appropriate for tag recommendation tasks based on text content.
The CountVectorizer function in scikit-learn is a common tool for text preprocessing, including stopword removal. It can be used to clean the blog post data by removing stopwords before using the NTM model for tag recommendation.
I deduce that the answer is:
Remove the stopwords from the blog post data by using the CountVectorizer function in the scikit-learn library. Replace the blog post data in the S3 bucket with the results of the vectorizer.
I appraise that the answer is:
Remove the stopwords from the blog post data by using the CountVectorizer function in the scikit-learn library. Replace the blog post data in the S3 bucket with the results of the vectorizer.