CUSTOMISED
Expert-led training for your team

Dismiss

Python & NLP training course

Learn how to write programs that analyze written language

Watch client feedback from Python training courses:

Public Courses

17/02/25 - 2 days

£1795 +VAT

Enrol

31/03/25 - 2 days

£1795 +VAT

Enrol

12/05/25 - 2 days

£1795 +VAT

Enrol

Customised Courses

* Train a team
* Tailor content
* Flex dates

From £1200 / day

Highlights Details Audience Feedback Prices/Dates Related Courses

Public Courses

Highlights

Process text data using Python tools
Extract important keywords and topics of interest from large text collections
Capture semantics using efficient text representations like word embeddings
Use Machine Learning techniques to categorise documents
Learn about the latest approaches to tackle advanced applications like language generation, entity recognition and text summarisation

Python Data Analysis Training Course London UK (Taster Video)

Course Details

1 - Foundations

This first section provides the basic tools and techniques to get started with Natural Language Processing

Overview on NLP applications and the Python

NLTK
spaCy
Gensim
scikit-learn

Working with text

Tokenisation
Text pre-processing
Regular Expressions

Word frequencies and co-occurrences

Stop-words and Zipf's Law
Mining topics of interest with co-occurrences

Text Representation

n-grams
Bag-of-words
Word embeddings and document embeddings

2 - Topic Modelling

This section aims at improving our understanding of a document, or a collection of documents, using techniques that go beyond simple word frequencies.

Topic Modelling

Bird's-eye view on a document or a dataset
Navigating topics and sub-topics in a document or a dataset

3 - Text Classification

This section tackles the problem of classifying documents into a set of predefined categories.

Categorising documents
Topic Classification
Sentiment Analysis
Model evaluation: assessing classification quality
Model introspection: explaining the classification results

4 - Overview on Advanced Applications

The last section offers an outlook on advanced NLP problems, so delegates are equipped with ideas and techniques to tackle more specific applications

Search Engines

Building a search engine to retrieve relevant documents from a custom data set of text

Named Entity Recognition

Identifying named entity in text

Text Summarisation

Extracting the most useful sentences from a document or a collection of documents

Natural Language Generation

Creating an AI conversational agent for customer service and similar

Who should attend

Software engineers, data scientists, data analysts, researchers and students who want to get started with Natural Language Processing applications, with the purpose of extracting useful information from free-text data.

Feedback

4.8 out of 5 average

Watch client feedback from Python training courses:

“JBI did a great job of customizing their syllabus to suit our business needs and also bringing our team up to speed on the current best practices. Our teams varied widely in terms of experience and the Instructor handled this particularly well - very impressive”

Brian F, Team Lead, RBS, Data Analysis Course, 20 April 2022

Top 20 "Pain Points" for Data Analysts

Problem 11 : You have a very complex Excel spreadsheet and you want to reproduce EXACTLY the same spreadsheet in Power BI
Solution: Power BI is not Excel, it works differently and it has different strengths. In order to tackle this issue the best way is going back to the source and try to...

All 20 points are in our latest Newsletter - Delivered directly to your inbox

More about this course

The course will balance theoretical foundations with practical examples using the Python programming language.

No prior experience with libraries such as NLTK or scikit-learn is required for this course.

Having existing experience with Python will be extremely beneficial but not required: users of other programming languages and tools (including e.g. Java, C++, C#, JavaScript, Matlab, Excel or Rlang) will find this course beneficial.