Natural Language Processing

About this course

Turn raw text into insight with Python. In two days you will read text and PDF files, search for patterns with regular expressions, tokenise, stem and lemmatise with spaCy and NLTK, tag parts of speech, recognise named entities, classify documents, analyse sentiment, model topics and see where deep learning fits into NLP.

What you'll learn

• Learn to work with Text Files with Python
• Learn how to work with PDF files in Python
• Utilize Regular Expressions for pattern searching in text
• Use Spacy for ultra fast tokenization
• Learn about Stemming and Lemmatization
• Understand Vocabulary Matching with Spacy
• Use Part of Speech Tagging to automatically process raw text files
• Understand Named Entity Recognition
• Visualize POS and NER with Spacy
• Use SciKit-Learn for Text Classification
• Use Latent Dirichlet Allocation for Topic Modelling
• Learn about Non-negative Matrix Factorization
• Use the Word2Vec algorithm
• Use NLTK for Sentiment Analysis
• Use Deep Learning to build out your own chat bot

Curriculum

Who it's for

Python users with data science experience who work with documents, feedback or other text data.

Before you start

• Introduction to Python and Python for Data Science ; Python for Machine Learning recommended