What do I need to know before starting?

The entry expectations are listed on this page. If you are close but not quite there, tell us at application; we run short pre-course preparation material and would rather get you ready than turn you away.

Does this course include the internship?

Yes, for learners who complete the core modules and meet the mid-point assessment bar. The internship is on live client work with a supervisor and code review, and you receive a written reference describing what you actually did.

Is there a certificate?

Yes, on completion of the capstone. More usefully, you finish with deployed projects, a public repository history, and a supervisor’s reference. Those are what interviews actually turn on.

What roles does Natural Language Processing prepare me for?

Typical destinations are NLP Engineer, Machine Learning Engineer and Data Scientist (text). The curriculum is built backwards from what those interviews and first ninety days actually require.

Academy track48 hours · Beginner to Intermediate

Natural Language Processing

Name: Natural Language Processing
Availability: InStock

From text preprocessing to embeddings and Transformers

Apply for this trackPreview the syllabus

Fee on request

Duration: 48 guided hours
Level: Beginner to Intermediate

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Next cohort intake is open · instalments available

Why this track

Turn raw text into something a model can use, then build and ship text applications. The course runs from cleaning and vectorizing text through classical classifiers and word embeddings to Transformers with Hugging Face, and ends in a deployed text-classification app.

48hours of guided, hands-on learning

6modules, each ending in something deployed

3portfolio projects, reviewed like client work

3+roles this track prepares you for

Outcomes

What you will be able to do

Preprocess and vectorize text appropriately
Build and evaluate classical text classifiers
Explain word embeddings, recurrent models and transformer basics
Deploy a practical text application using Streamlit and GitHub

Before you start

Entry expectations

If you are close but not quite there, tell us at application. We would rather get you ready than turn you away.

01Basic Python and pandas
02Introductory ML concepts, especially classification (the Machine Learning course is recommended)

Tools you will use

PythonpandasspaCyNLTKscikit-learnHugging FaceStreamlitGit & GitHub

Syllabus

A first look at the route

6 modules over 48 guided hours. Here is how it starts; the full roadmap opens up when you join.

M01Text data and preprocessing
- NLP tasks, corpora, labels and train/test design
- Cleaning, normalization, tokenization and stop words
- Stemming vs lemmatization; spaCy and NLTK
- Bag of Words, n-grams and TF-IDF
- Text preprocessing pipeline and error cases
M02Classical ML for NLP
- Spam/text classification with Naive Bayes
- Logistic regression and linear SVM for text
- Sentiment analysis and label imbalance
- Metrics: precision, recall, F1, confusion matrix
- Compare vectorizers/models with cross-validation
- Deploy a text-classification app in Streamlit

4 more modules in the full roadmap

The complete syllabus, with every topic, hands-on lab and the capstone brief, is shared with learners when they join the course.

Join the course

Portfolio

What you will have built

01A sentiment, spam, support-ticket or review-classification application with preprocessing, baseline comparison and evaluation
02A semantic similarity / embedding explorer demo
03Transformer inference deployed with Streamlit

Where it leads

Roles this prepares you for

NLP EngineerMachine Learning EngineerData Scientist (text)

Questions

Before you apply

The course is 48 guided hours of explanation, guided coding and review, plus independent practice; admissions will share the cohort schedule. Live sessions are scheduled outside standard office hours and recorded, so working professionals can keep a full-time job while studying.