Words do not equal signals
Choose representations and features that match the task and available data.
Course 07 · 5 days
NLP & Text Intelligence
Build practical text intelligence systems across representation, transformers, embeddings, LLMs, RAG, and production NLP workflows.
1 · Learner problem
Text is rich data, but useful intelligence needs the right representation and evaluation.
NLP builders must choose between classical text analytics, modern transformers, embeddings, LLMs, and RAG while keeping the use case measurable.
Modern models need context
Understand transformers, embeddings, and LLM patterns without losing task discipline.
Text quality is contextual
Define useful evaluation for classification, search, generation, and semantic workflows.
A production text system
Connect text intelligence methods in a capstone with clear next engineering steps.
2 · Course outcome
Leave able to choose, build, and explain a text intelligence approach.
Understand text data
Explore text analytics, representation, and task framing.
Use modern NLP
Compare transformers, embeddings, LLMs, and retrieval patterns.
Measure useful output
Define task-appropriate evaluation for text intelligence systems.
Plan production NLP
Show architecture, quality checks, deployment concerns, and capstone value.
For NLP and AI developers working with text.
- NLP and AI developers
- Software or ML engineers working with language data
- Analysts building text classification or search
- Builders with a corpus and a text intelligence problem
Bring text and a question.
Basic Python or ML familiarity helps. The course is applied and connects methods to a demonstrable text workflow.
4 · Curriculum
Eight modules from foundations to production NLP.
- NLP Foundations & Text AnalyticsFrame tasks and inspect language data.
- Text Representation & MLUse representations, features, and machine learning for text.
- Transformers & Modern NLPUnderstand transformer workflows and modern model choices.
- Embeddings & Semantic Text IntelligenceUse embeddings for similarity, search, and semantic tasks.
- Generative AI, LLMs & RAGConnect language models to context and trusted knowledge.
- Advanced Text IntelligenceCompare advanced patterns for real text workflows.
- Production NLP + CapstonePackage the methods into a production-minded project.
5 · Live project
Build a production NLP or text intelligence workflow.
Choose classification, semantic search, document intelligence, support knowledge, or a retrieval-augmented text application.
Text and task framing
Define corpus, user, labels or queries, output, and quality bar.
Representation and model path
Choose features, embeddings, transformers, LLMs, or RAG for the workflow.
Production NLP demo
Show evaluation, failure cases, architecture, and the next deployment step.
6 · Trainer credentials
View trainer profileText intelligence taught with practical evaluation.
MHGrad’s practice covers NLP, GenAI applications, RAG, agents, analytics, and responsible AI workflows. Verified trainer credentials and references are available during enrolment conversations.
Five-day live intensive
- Daily guided NLP instruction and build time
- Model and evaluation checkpoints
- Production NLP capstone demonstration
- Exact dates, timezone, and delivery format confirmed before enrolment
8–10 · Fees and enrolment
Current fee shared on consultation.
Ask about the next intensive, availability, payment steps, or private team delivery. Refund terms are published before payment.
Enquire for current fee
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FAQ and refund terms
Before you enrol.
Do I need deep NLP theory?
The course is applied. Basic programming or ML familiarity helps while the modules connect foundations to modern text workflows.
Can I bring my own text corpus?
Only use data you are authorised to share. A guided corpus is available when company data cannot be used.
Is RAG included?
Yes. Generative AI, LLMs, and RAG are part of the curriculum alongside classical and modern NLP approaches.
What is the refund policy?
Eligibility, cut-off dates, transfers, and cancellations are stated in the refund policy and enrolment confirmation.
Turn text into a system people can use.
Ask for dates, fee, prerequisites, or a team-training conversation.