Fundamentals of NLP: Representing Text as Numeric Features

Natural Language Processing    |    Intermediate
  • 15 videos | 2h 17s
  • Includes Assessment
  • Earns a Badge
Rating 4.5 of 2 users Rating 4.5 of 2 users (2)
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

  • 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

  • 2m 8s
    In this video, we will discover the key concepts covered in this course. FREE ACCESS
  • 7m 26s
    After 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 Data
    10m 5s
    Find out how to perform one-hot encoding on text. FREE ACCESS
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    4.  Performing One-hot Encoding Using the Count Vectorizer
    7m 6s
    Discover 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 Form
    5m 47s
    Upon 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 Vectorizer
    4m 56s
    In 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 Representation
    12m 2s
    In 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 Data
    11m 37s
    In 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 Dataset
    11m 28s
    Learn how to explore and analyze data. FREE ACCESS
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    10.  Building a Classification Model Using Count Vector Encoding
    10m
    During 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 Removed
    8m 40s
    Discover 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 Encodings
    8m 38s
    Find out how to filter words based on frequency for classification. FREE ACCESS
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    13.  Training Classification Models Using Bag of N-grams
    10m 14s
    Learn how to train classification models on n-grams. FREE ACCESS
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    14.  Training Classification Models with N-grams and TF-IDF Representation
    7m 16s
    In this video, you will learn how to train models on TF-IDF encodings. FREE ACCESS
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    15.  Course Summary
    2m 54s
    In this video, we will summarize the key concepts covered in this course. FREE ACCESS

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