What you'll learn

Companies generate massive amounts of unstructured text, but many engineering teams lack the foundational knowledge needed to represent and process this data effectively for machine learning. In this course, Natural Language Processing for Deep Learning, you’ll gain the ability to transform raw text into meaningful numerical representations and choose the right NLP approach for real-world applications. First, you’ll explore how to preprocess and structure text through tokenization, normalization, and other essential cleaning techniques. Next, you’ll discover how to convert text into machine-readable features using classic vectorization methods, static word embeddings, and modern contextual embeddings. Finally, you’ll learn how to apply these representations within deep learning models and understand when to use classical, static, or contextual approaches based on your use case. When you’re finished with this course, you’ll have the skills and knowledge of foundational NLP needed to build reliable and performant text-driven features in your applications.