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

Audience

This course is designed for data scientists, machine learning engineers, and software developers interested in enhancing their NLP skills. A basic understanding of Python programming is needed, but no prior knowledge of NLP is required.

About the Author

Lazy Programmer: The Lazy Programmer, a distinguished online educator, boasts dual master's degrees in computer engineering and statistics, with a decade-long specialization in machine learning, pattern recognition, and deep learning, where he authored pioneering courses. His professional journey includes enhancing online advertising and digital media, notably increasing click-through rates and revenue. As a versatile full-stack software engineer, he excels in Python, Ruby on Rails, C++, and more. His expansive knowledge covers areas like bioinformatics and algorithmic trading, showcasing his diverse skill set. Dedicated to simplifying complex topics, he stands as a pivotal figure in online education, adeptly navigating students through the nuances of data science and AI.