Natural Language Processing – Embeddings and Text Preprocessing in Python

Natural Language Processing – Embeddings and Text Preprocessing in Python
MP4 | Video: AVC 1280×720 | Audio: AAC 44KHz 2ch | Duration: 6 Hour | 1.17 GB

As you progress, you’ll delve deeper into advanced vector models. Learn about the Bag of Words model, Count Vectorizer, and TF-IDF, both in theory and through hands-on coding demonstrations. You’ll also explore the fascinating world of vector similarity and word-to-index mapping, equipping you with the knowledge to handle complex text data. An interactive exercise on recommender systems will challenge you to apply these concepts in a practical scenario.

What you will learn

Understand and apply basic text preprocessing techniques
Implement Bag of Words, Count Vectorizer, and TF-IDF models
Conduct stemming, lemmatization, and stopword removal
Explore vector similarity and word-to-index mapping
Utilize neural word embeddings in NLP applications
Build and evaluate text recommender systems using TF-IDF