Fundamentals of NLP: Representing Text as Numeric Features
Natural Language Processing
| Intermediate
- 15 videos | 2h 17s
- Includes Assessment
- Earns a Badge
When performing sentiment classification using machine learning, it is necessary to encode text into a numeric format because machine learning models can only parse numbers, not text. There are a number of encoding techniques for text data, such as one-hot encoding, count vector encoding, and word embeddings. In this course, you will learn how to use one-hot encoding, a simple technique that builds a vocabulary from all words in your text corpus. Next, you will move on to count vector encoding, which tracks word frequency in each document and explore term frequency-inverse document frequency (TF-IDF) encoding, which also creates vocabularies and document vectors but uses a TF-IDF score to represent words. Finally, you will perform sentiment analysis using encoded text. You will use a count vector to encode your input data and then set up a Gaussian Naïve-Bayes model. You will train the model and evaluate its metrics. You will also explore how to improve the model performance by stemming words, removing stopwords, and using N-grams.
WHAT YOU WILL LEARN
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Discover the key concepts covered in this course
Understand one-hot encoding representations
Perform one-hot encoding on text
Use the countvectorizer object for one-hot encoding
Outline how to encode text based on frequencies
Encode text as count vectors
Explore bag-of-words and bag-of-ngrams encoding
Encode data using term frequency–inverse document frequency (tf-idf) scores -
Explore and analyze data
Create a naive-bayes model for sentiment analysis
Stem words and remove stopwords for machine learning
Filter words based on frequency for classification
Train classification models on n-grams
Train models on tf-idf encodings
Summarize the key concepts covered in this course
IN THIS COURSE
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2m 8sIn this video, we will discover the key concepts covered in this course. FREE ACCESS
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7m 26sAfter completing this video, you will be able to understand one-hot encoding representations. FREE ACCESS
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3. Utilizing One-hot Encoding to Represent Text Data10m 5sFind out how to perform one-hot encoding on text. FREE ACCESS
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4. Performing One-hot Encoding Using the Count Vectorizer7m 6sDiscover how to use the CountVectorizer object for one-hot encoding. FREE ACCESS
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5. Frequency-based Encodings to Represent Text in Numeric Form5m 47sUpon completion of this video, you will be able to outline how to encode text based on frequencies. FREE ACCESS
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6. Perform Count Vector Encoding Using the Count Vectorizer4m 56sIn this video, you will learn how to encode text as count vectors. FREE ACCESS
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7. Working with Bag-of-Words and Bag-of-N-grams Representation12m 2sIn this video, we will explore bag-of-words and bag-of-n-grams encoding. FREE ACCESS
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8. Perform TF-IDF Encoding to Represent Text Data11m 37sIn this video, find out how to encode data using term frequency–inverse document frequency (TF-IDF) scores. FREE ACCESS
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9. Exploring the Product Reviews Dataset11m 28sLearn how to explore and analyze data. FREE ACCESS
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10. Building a Classification Model Using Count Vector Encoding10mDuring this video, you will discover how to create a Naive-Bayes model for sentiment analysis. FREE ACCESS
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11. Comparing Models Trained with Stemmed Words and Stopword Removed8m 40sDiscover how to stem words and remove stopwords for machine learning. FREE ACCESS
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12. Classifying Text Using Frequency Filtering and TF-IDF Encodings8m 38sFind out how to filter words based on frequency for classification. FREE ACCESS
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13. Training Classification Models Using Bag of N-grams10m 14sLearn how to train classification models on n-grams. FREE ACCESS
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14. Training Classification Models with N-grams and TF-IDF Representation7m 16sIn this video, you will learn how to train models on TF-IDF encodings. FREE ACCESS
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15. Course Summary2m 54sIn this video, we will summarize the key concepts covered in this course. FREE ACCESS
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