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
"The course was professionally run and I liked that it is interactive with exercises of how AI is used. The instructor is very knowledgeable on the subject and enthusiastic about machine learning"
YZ, Software developer, Python AI & ML, May 2021
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