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Python & NLP training course

Learn how to write programs that analyze written language

4.8 out of 5 average

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Overview

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.

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PUBLIC COURSES
Next on 20th Jun 2022

Highlights Course detail Who should attend Feedback Prices/Dates Related 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)

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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

TAILOR-MADE
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PUBLIC COURSES
Next on 20 Jun - see prices

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.

TAILOR-MADE
Enquire & get a quote

PUBLIC COURSES
Next on 20 Jun - see prices