Natural Language Processing - Embeddings and Text Preprocessing in Python: Master text preprocessing and vector models in Python for powerful NLP applications [Video]

Name: Natural Language Processing - Embeddings and Text Preprocessing in Python [Video]
Brand: Packt
SKU: 9781836208754
Price: 82.99 EUR
Availability: InStock

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Video Jun 2024 6 hours 8 minutes 1st Edition

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Key benefits

From basic NLP definitions to advanced vector models and neural embeddings
Practical coding demos and interactive exercises for real-world application
These topics provide a robust understanding of NLP essential for tackling complex NLP tasks and enhancing data processing skills

Description

In this course, you will embark on a journey through the fundamental concepts and practical applications of Natural Language Processing (NLP) in Python. Starting with basic definitions, you'll quickly move into understanding the importance of vector models in NLP. Our videos will guide you through essential techniques such as tokenization, stemming, lemmatization, and the use of stopwords, ensuring you grasp the intricacies of text preprocessing. 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. The course culminates with an introduction to neural word embeddings, providing a glimpse into the future of NLP. You'll see these powerful techniques in action and understand how they can be applied to various languages beyond English. Additionally, the course includes valuable resources on setting up your Python environment and extra help with Python coding, making it suitable for learners at different skill levels.

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