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| Archived
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01 - Course Overview\01 - Course Overview.mp4
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02 - Processing and Simplifying Text to Simplify Feature Creation\02 - Module Overview.mp4
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02 - Processing and Simplifying Text to Simplify Feature Creation\03 - Prerequisites.mp4
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4,626,864 |
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02 - Processing and Simplifying Text to Simplify Feature Creation\04 - Demo - Configure AMLS.mp4
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02 - Processing and Simplifying Text to Simplify Feature Creation\05 - Preprocessing and NLP.mp4
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02 - Processing and Simplifying Text to Simplify Feature Creation\06 - Tokenization and Cleaning.mp4
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02 - Processing and Simplifying Text to Simplify Feature Creation\07 - Demo - Sentence and Word Tokenization.mp4
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02 - Processing and Simplifying Text to Simplify Feature Creation\08 - Demo - NLTK Tokenizers.mp4
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02 - Processing and Simplifying Text to Simplify Feature Creation\09 - Demo - Token Cleaning.mp4
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02 - Processing and Simplifying Text to Simplify Feature Creation\10 - Stopword Removal.mp4
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02 - Processing and Simplifying Text to Simplify Feature Creation\11 - Demo - Stopword Removal.mp4
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02 - Processing and Simplifying Text to Simplify Feature Creation\12 - Frequency Filtering.mp4
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02 - Processing and Simplifying Text to Simplify Feature Creation\13 - Demo - Frequency Filtering.mp4
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02 - Processing and Simplifying Text to Simplify Feature Creation\14 - Stemming.mp4
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02 - Processing and Simplifying Text to Simplify Feature Creation\15 - Demo - Stemming.mp4
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02 - Processing and Simplifying Text to Simplify Feature Creation\16 - Parts-of-speech Tagging.mp4
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02 - Processing and Simplifying Text to Simplify Feature Creation\17 - Demo - Parts-of-speech.mp4
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02 - Processing and Simplifying Text to Simplify Feature Creation\18 - Lemmatization.mp4
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02 - Processing and Simplifying Text to Simplify Feature Creation\19 - Demo - Lemmatization.mp4
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02 - Processing and Simplifying Text to Simplify Feature Creation\20 - N-grams.mp4
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02 - Processing and Simplifying Text to Simplify Feature Creation\21 - Demo - N-grams.mp4
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02 - Processing and Simplifying Text to Simplify Feature Creation\22 - Module Summary.mp4
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03 - Building Features Around Text Data for Use in Machine Learning Models\23 - Module Overview.mp4
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03 - Building Features Around Text Data for Use in Machine Learning Models\24 - Encoding Text as Numbers.mp4
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03 - Building Features Around Text Data for Use in Machine Learning Models\25 - One-hot and Count Vector Encoding.mp4
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03 - Building Features Around Text Data for Use in Machine Learning Models\26 - Demo - Bag-of-words.mp4
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03 - Building Features Around Text Data for Use in Machine Learning Models\27 - Demo - Bag-of-n-grams.mp4
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03 - Building Features Around Text Data for Use in Machine Learning Models\28 - TF-IDF Encoding.mp4
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03 - Building Features Around Text Data for Use in Machine Learning Models\29 - Demo - TF-IDF Encoding.mp4
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03 - Building Features Around Text Data for Use in Machine Learning Models\30 - Word Embeddings.mp4
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03 - Building Features Around Text Data for Use in Machine Learning Models\31 - Demo - Word Embeddings Using Word2Vec.mp4
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03 - Building Features Around Text Data for Use in Machine Learning Models\32 - Feature Hashing.mp4
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03 - Building Features Around Text Data for Use in Machine Learning Models\33 - Demo - The Hashing Trick.mp4
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03 - Building Features Around Text Data for Use in Machine Learning Models\34 - Locality-sensitive Hashing.mp4
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03 - Building Features Around Text Data for Use in Machine Learning Models\35 - Demo - Locality-sensitive Hashing.mp4
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03 - Building Features Around Text Data for Use in Machine Learning Models\36 - BERT.mp4
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03 - Building Features Around Text Data for Use in Machine Learning Models\37 - Demo - Word Embeddings with BERT on AMLS.mp4
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03 - Building Features Around Text Data for Use in Machine Learning Models\38 - Summary.mp4
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microsoft-azure-building-features-text-data.zip |
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01 - Course Overview |
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02 - Processing and Simplifying Text to Simplify Feature Creation |
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03 - Building Features Around Text Data for Use in Machine Learning Models |
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| Total size: |
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