Natural Language Processing - Embeddings and Text Preprocessing in Python

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