Applied Text Mining in Python

This course will introduce the learner to text mining and text manipulation basics. The course begins with an understanding of how text is handled by python, the structure of text both to the machine and to humans, and an overview of the nltk framework for manipulating text.

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Key Points About This Course

Duration: X Days
Time: 9.00am-5.00pm
Public Class Fee: RM X,XXX.XX
Virtual Class Fee: RM X,XXX.XX
HRDF Claimable

Course Overview

This course will introduce the learner to text mining and text manipulation basics. The course begins with an understanding of how text is handled by python, the structure of text both to the machine and to humans, and an overview of the nltk framework for manipulating text. The second week focuses on common manipulation needs, including regular expressions (searching for text), cleaning text, and preparing text for use by machine learning processes. The third week will apply basic natural language processing methods to text, and demonstrate how text classification is accomplished. The final week will explore more advanced methods for detecting the topics in documents and grouping them by similarity (topic modelling).

This course should be taken after: Introduction to Data Science in Python, Applied Plotting, Charting & Data Representation in Python, and Applied Machine Learning in Python.

What You Will Learn

  • Understand how text is handled in Python
  • Write code that groups documents by topic
  • Apply basic natural language processing methods
  • Describe the nltk framework for manipulating text

Skills You Will Gain

  • Natural Language Toolkit (NLTK)
  • Text Mining
  • Python Programming
  • Natural Language Processing

Course Content

Part 1: Working with Text in Python
  • Introduction to Text Mining
  • Handling Text in Python
  • Regular Expressions
  • Demonstration: Regex with Pandas and Named Groups
  • Internationalization and Issues with Non-ASCII Characters
Part 2: Basic Natural Language Processing
  • Basic Natural Language Processing
  • Basic NLP tasks with NLTK
  • Advanced NLP tasks with NLTK
Part 3: Classification of Text
  • Text Classification
  • Identifying Features from Text
  • Naive Bayes Classifiers
  • Naive Bayes Variations
  • Support Vector Machines
  • Learning Text Classifiers in Python
  • Demonstration: Case Study – Sentiment Analysis
Part 4: Topic Modeling
  • Semantic Text Similarity
  • Topic Modeling
  • Generative Models and LDA
  • Information Extraction

Training Schedule

15 – 17 Feb 2021
12 – 14 Apr 2021
8 – 10 Jun 2021
2 – 5 Aug 2021
25 – 27 Oct 2021
13 – 15 Dec 2021

  • Public Class Training

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  • Examination (Optional)

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