Natural Language Processing (NLP) and Text Processing Foundations

Transform raw text into usable data with NLP

Before language models can classify, interpret, or predict from text, that text needs to be prepared properly.

On this introductory course, you’ll explore the foundations of Natural Language Processing (NLP) and learn how to turn messy, unstructured language into data that can be analysed and modelled.

Start by examining how human language is structured and why text presents unique challenges for AI.

You’ll then work through practical techniques for acquiring, cleaning, and normalising text, including encoding, case handling, noise removal, and regular expressions.

Structure language for analysis

Explore tokenisation, stemming, lemmatisation, and stop-word handling to break text into meaningful components.

You’ll also work with part-of-speech tagging, syntactic structure, and n-grams to better understand how linguistic patterns can be represented computationally.

Convert text into numerical features

Discover how techniques such as bag-of-words, count vectors, and TF-IDF transform language into numerical representations that machine learning models can use.

You’ll examine the strengths and limitations of different approaches and see how feature design influences downstream NLP tasks.

Assemble a reusable NLP pipeline

Bring your learning together by creating a reproducible text preprocessing workflow and applying it to classical NLP tasks such as keyword extraction and text matching.

By the end, you’ll be able to clean, structure, and represent text confidently, giving you the foundation needed to progress into text classification, sentiment analysis, and more advanced NLP applications.

This ExpertTrack is ideal for aspiring NLP engineers, data scientists, machine learning practitioners, Python developers, and analysts who want a practical introduction to processing text data. Basic Python knowledge is recommended; no prior NLP experience is required.

What software or tools do you need? No prior natural language processing or text classification experience is required, although a basic familiarity with Python will help you follow the demonstrations. The course introduces the tools and setup you need from the beginning, including the Python libraries and datasets used in the practical activities. You will need access to a suitable computer, a stable internet connection and the relevant online accounts to follow the hands-on exercises.

This ExpertTrack is ideal for aspiring NLP engineers, data scientists, machine learning practitioners, Python developers, and analysts who want a practical introduction to processing text data. Basic Python knowledge is recommended; no prior NLP experience is required.

• Explain how NLP uses linguistic concepts such as morphology, syntax, semantics, and pragmatics to process human language.
• Apply text cleaning, regular expressions, and normalisation techniques to prepare raw text for NLP tasks.
• Compare tokenisation, stemming, and lemmatisation approaches for reducing variation in language data.
• Develop text representations using Bag of Words, TF-IDF, and word embeddings such as Word2Vec, GloVe, and FastText.
• Build and evaluate classical text classification models using Naïve Bayes, SVM, and standard performance metrics.