Building Features from Text Data

سرفصل های دوره

This course covers aspects of extracting information from text documents and constructing classification models including feature vectorization, locality-sensitive hashing, stopword removal, lemmatization, and more from natural language processing.

1. Course Overview

1. Course Overview

2. Representing Text as Features for Machine Learning

01. Version Check

02. Module Overview

03. Prerequisites and Course Outline

04. One-hot Encoding

05. Count Vectors

06. Tf-Idf Vectors

07. Co-occurence Vectors

08. Word Embeddings

09. Installing Packages and Setting Up the Environment

10. Sentence and Word Tokenization

11. Plotting Word Frequency Distributions

12. Module Summary

3. Building Feature Vector Representations of Text

1. Module Overview

2. Bag-of-words and Bag-of-n-grams

3. Bag-of-words Using the Count Vectorizer

4. Inverse Transform Using the Count Vectorizer

5. Bag-of-n-grams Using the Count Vectorizer

6. Generating N-grams Using NLTK

7. Bag-of-words Using the Tf-Idf Vectorizer

8. Module Summary

4. Simplifying Text Processing Using Natural Language Processing

1. Module Overview

2. Natural Language Processing Operations

3. Stopword Removal Using NLTK and scikit-learn

4. Frequency Filtering Using scikit-learn

5. Stemming

6. Lemmatization

7. Parts-of-speech Tagging

8. Module Summary

5. Reducing Dimensions in Text Using Hashing

1. Module Overview

2. Feature Hashing

3. Reducing Dimensions Using the Feature Hasher

4. Reducing Dimensions at Scale Using the Hashing Vectorizer

5. Locality-sensitive Hashing

6. Similar Documents Using Jaccard Index and Locality-sensitive Hashing

7. Module Summary

6. Applying Text Feature Extraction Techniques to Machine Learning

01. Module Overview

02. Naive Bayes for Classification

03. Classification Using the Hashing Vectorizer

04. Pre-process Text Using a Stemmer, Build Features Using the Hashing Vectorizer

05. Building Features Using the Count Vectorizer

06. Pre-processing with Stopword Removal, Building Features Using Count Vectorizer

07. Pre-processing with Stopword Removal, Frequency Filtering, Building Features Using Count Vectorizer

08. Building Features Using the Tf-Idf Vectorizer

09. Building Features Using Bag-of-n-grams Model

10. Summary and Further Study

45,900 تومان

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تولید کننده: PluralSight