Fundamentals of NLP: Word Embeddings to Capture Relationships in Text
Natural Language Processing
| Intermediate
- 8 videos | 1h 42s
- Includes Assessment
- Earns a Badge
Before training any text-based machine learning model, it is necessary to encode that text into a machine-readable numeric form. Embeddings are the preferred way to encode text, as they capture data about the meaning of text, and are performant even with large vocabularies. You will start this course by working with Word2Vec embeddings, which represent words and terms in feature vector space, capturing the meaning and context of a word in a sentence. You will generate Word2Vec embeddings on your data corpus, set up a Gaussian Naïve-Bayes classification model, and train it on Word2Vec embeddings. Next, you will move on to GloVe embeddings. You will use the pre-trained GloVe word vector embeddings and explore how to view similar words and identify the odd one out in a set. Finally, you will perform classification using many different models, including Naive-Bayes and Random Forest models.
WHAT YOU WILL LEARN
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Discover the key concepts covered in this courseOutline how word embeddings workGenerate word2vec text embeddingsTrain a classification model on text embeddings
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Work with pre-trained glove embeddingsTrain a gaussian naive bayes model on glove embeddingsTrain classification models for sentiment analysisSummarize the key concepts covered in this course
IN THIS COURSE
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2m 12sIn this video, we will discover the key concepts covered in this course. FREE ACCESS
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3m 45sAfter completing this video, you will be able to outline how word embeddings work. FREE ACCESS
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12mFind out how to generate Word2Vec text embeddings. FREE ACCESS
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11m 6sDiscover how to train a classification model on text embeddings. FREE ACCESS
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10m 49sIn this video, you will learn how to work with pre-trained GloVe embeddings. FREE ACCESS
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8m 51sDuring this video, you will discover how to train a Gaussian Naive Bayes model on GloVe embeddings. FREE ACCESS
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8m 51sLearn how to train classification models for sentiment analysis. FREE ACCESS
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3m 9sIn this video, we will summarize the key concepts covered in this course. FREE ACCESS
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