Most organisational knowledge is unstructured. Customer complaints, call transcripts, policy documents, case notes and public commentary all arrive as words, and manual reading cannot keep pace with their volume. Natural language processing allows analysts to classify, search, summarise and measure text consistently, and recent advances in transformer models have made capable tools available to anyone who can write a little Python.

This ten-day course takes analysts from raw text to deployed insight. Early days build a solid foundation in text preprocessing, feature extraction and supervised learning, so that results can be explained and trusted. Participants then add sentiment analysis, topic discovery, entity extraction and embeddings, learning how to evaluate each technique honestly and spot errors such as data leakage and class imbalance. The later days introduce pretrained and fine-tuned transformer models, summarisation, semantic search and retrieval-based question answering, along with the ethical, privacy and bias issues that arise when models are applied to people's words.

Every day combines a short concept session with extended coding labs in notebooks. The closing project asks each participant to define a text problem, build and evaluate a solution and present findings to peers in plain language.

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

• Clean text and represent documents with spaCy, TF-IDF and word embeddings.
• Train and evaluate text classifiers using precision, recall and F1.
• Perform sentiment analysis and interpret its limits.
• Discover themes with topic modelling and clustering.
• Extract people, organisations, places and dates with named entity recognition.
• Apply pretrained transformer models for classification, summarisation and question answering.
• Assess bias, privacy and reliability risks in language models.

The course is designed for analytical professionals with basic Python or data skills, including:

• Data analysts and business intelligence specialists
• Data scientists and machine learning engineers
• Customer insight and market research analysts
• Risk, compliance and audit analysts who review documents
• Monitoring, evaluation and research officers
• Journalists and media monitoring teams
• Software developers adding language features to products
• Statisticians and academic researchers working with text

Participants leave the course with:

• A reusable notebook for cleaning and preparing text data.
• A trained and evaluated classifier for their own use case.
• A topic and sentiment analysis workflow for open-ended feedback.
• An entity extraction script for structured records from documents.
• A working semantic search prototype.
• A checklist for judging model quality, bias and privacy risk.
• A completed capstone project they can show to colleagues.

The programme is laboratory-driven and paced for working analysts:

• Short explanatory sessions followed by coding in Jupyter notebooks
• Datasets drawn from reviews, news, policy documents and public feedback
• Pair programming to share debugging skills
• Weekly checkpoint reviews of evaluation results
• A capstone project with individual coaching from the trainer

Vision Reach Global Consultancy issues a CPD-accredited Certificate of Completion to participants who attend the ten days and complete the lab exercises and capstone project.