Applied Natural Language Processing: From Text Data to Intelligent Applications

Ideal For

This course is designed for:

• Software developers and engineers who want to build intelligent text-processing features and NLP-powered products for Nigerian and African markets

• Data scientists with general machine learning experience who want to specialise in natural language processing and text analytics

• Business analysts and market researchers who work with large volumes of text data — customer feedback, survey responses, social media mentions — and want to analyse it systematically and at scale

• Customer experience and CRM professionals in banking, telecoms, and retail who want to deploy sentiment analysis, complaint classification, and customer intelligence tools

• Media, journalism, and communications professionals who work with news data, social media content, and public discourse and want to apply NLP to content analysis and monitoring

• Legal and compliance professionals handling large volumes of contracts, regulations, and policy documents who want to automate document review and information extraction

• Healthcare and public health professionals working with patient records, clinical notes, and health communications data who want to apply NLP to healthcare information systems

• Government and policy professionals working with public submissions, legislation, and citizen communications who want to build text analytics capabilities

• Technical founders and product managers building NLP-powered products — chatbots, document processors, search systems, and content tools — for African markets

Recommended prior knowledge: Comfortable with Python — functions, loops, classes, and basic scripting. Familiarity with Pandas and NumPy is helpful. No prior NLP or deep learning experience required.

Summary of Learning  

By the end of this course, participants will be able to:

• Build complete text cleaning and preprocessing pipelines — handling Nigerian and African text including Pidgin English, code-switching, multilingual content, and non-standard spelling — transforming raw noisy text into structured machine-readable representations

• Apply classical and modern text representation techniques — including TF-IDF, Word2Vec, GloVe, FastText, and sentence embeddings — and select the right representation strategy for a given NLP task

• Build, train, and evaluate multi-class text classification systems using both classical machine learning models and deep learning approaches — including handling class imbalance and producing fully documented performance reports

• Implement lexicon-based, machine learning, and aspect-based sentiment analysis systems — applied to Nigerian social media, customer feedback, and news text with cultural and linguistic context built into the approach

• Design and train custom Named Entity Recognition (NER) models using spaCy — extracting Nigerian-specific entities including companies, executives, financial figures, and regulatory bodies from domain text

• Build deep learning NLP models using bidirectional LSTMs and sequence-to-sequence architectures — and explain the architectural trade-offs compared to transformer-based approaches

• Fine-tune pre-trained BERT models on custom Nigerian NLP tasks — including text classification, named entity recognition, and question answering — achieving high accuracy with limited labelled training data

• Apply multilingual transformer models including mBERT and XLM-RoBERTa to process Yoruba, Hausa, Igbo, and other African languages with limited training resources

• Design production NLP system architectures — including text search, semantic similarity, document summarisation, and chatbot pipelines — with appropriate latency, scalability, and reliability considerations

• Build and deploy a complete NLP application as a production REST API — wrapping a full NLP pipeline in FastAPI, containerising with Docker, and deploying to a live cloud endpoint with monitoring and maintenance guidance

Course Description

Participants will learn to preprocess and engineer features from raw text, build classical and deep learning NLP models, implement sentiment analysis, text classification, and named entity recognition systems, work with transformer models including BERT, and deploy production-ready NLP applications — all applied to Nigerian and African language datasets and business scenarios.