Exposing and Classifying Unique Words in A Document | Intro to NLP: Part 2 скачать в хорошем качестве

Exposing and Classifying Unique Words in A Document | Intro to NLP: Part 2

Presented by WWCode Data Science 👩‍💻 Speaker: Rishika Singh ✅ Topics: Part 2- TF-IDF, Bag-of-Words, Document Similarity Use Cases & Topic Modeling 📥 Download the slides from this session at http://bit.ly/introtonlp-week3-slides 🗒️ Access the Python Notebook from this session at https://bit.ly/introtonlp-week3-notebook Considering the vast amount of unstructured text data that is constantly generated (eg, social media channels like Facebook, Twitter, forums like Quora, Reddit, various blogs, news platforms), NLP has become the crux to process, analyze and understand this large amount of text data. Almost all technology we use daily has underlying NLP use-cases like speech-to-text, machine translation, image captioning and so on. This 6-part technical series (led by Jayeeta Putatunda, Rishika Singh, Candice Yun Chen, Ruchika Singh) introducing Natural Language Processing focuses on how it can be used in solving various industry problems! Register for upcoming sessions of this series at http://bit.ly/introtonlp-register In this session, the speaker continues our NLP Deep Dive feature engineering strategies, specifically TF-IDF, Bag-of-Words model and various use cases of Document Similarity and implement these concepts with nltk, sklearn and Google Colab. 💬 Join our Slack channel for community support and more http://bit.ly/wwcodedatascience-joins... 🔗 Links to all our FREE events (and registration) and social media can be found at https://linktr.ee/wwcodedatascience 🎥 Check our recordings of our past events at https://bit.ly/introtonlp-recordings