The Knowledge Academy

Natural Language Processing (NLP) Fundamentals With Python - Hong Kong

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Course Information

Schedules
  • 18 Nov 2021 (Thu) - 19 Nov 2021 (Fri) 9:00 AM - 5:00 PM
  • 16 Dec 2021 (Thu) - 17 Dec 2021 (Fri) 9:00 AM - 5:00 PM
  • 6 Jan 2022 (Thu) - 7 Jan 2022 (Fri) 9:00 AM - 5:00 PM
  • 20 Jan 2022 (Thu) - 21 Jan 2022 (Fri) 9:00 AM - 5:00 PM
Registration period
24 Sep 2021 (Fri) - 15 Dec 2021 (Wed)
Price
HKD 21,995
Course Level
Study Mode
Duration
2 Day(s)
Language
English
Location
-
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Course Overview

Who should attend this NLP course?

Anyone who wishes to gain knowledge regarding Natural Language Processing can attend this course. This course is ideal for:

  • Data Scientists
  • Developers who wish to become a Data Scientist
  • Python Professionals
  • Programmers and Data Analysts
  • Analytics Managers who are leading a team of Analysts

Prerequisites

Basic knowledge of Python is recommended.

Natural Language Processing (NLP) Fundamentals with Python Course Overview

Natural Language Processing (NLP) is a powerful skill that helps you extract important information from text data. The NLP Fundamentals with Python Training course guides delegates how to auto-summarise the text by using machine learning. Delegates will also acquire knowledge about how to build the text classifier using the Naive Bayes algorithm.

During this course, delegated will learn how to use the Natural Language Toolkit (NLTK) to pre-process raw text. You will master how to use NLTK with other Python Libraries such as SciPy, matplotlib, NumPy, and pandas.

What's included in this NLP training course?

  • Delegate pack consisting of course notes and exercises
  • Manual
  • Experienced Instructor
  • Refreshments

 

What You’ll Learn

Natural Language Processing (NLP) Fundamentals with Python Course Outline

  • Introduction to Natural Language Processing (NLP)
  • Overview of Python
  • Text Wrangling and Cleansing
    • Overview of Text Wrangling and Text Cleansing
    • Sentence Splitter
    • Tokenisation
    • Stemming
    • Lemmatisation
  • POS Tagging
    • Stanford Tagger
    • Sequential Tagger
    • Brill Tagger
    • Machine Learning Based Tagger
  • Parsing Structure in Text
  • Natural Language Processing Applications
  • Text Classification
    • Naive Bayes
    • Decision Trees
    • Stochastic Gradient Descent
    • Logistic Regression
    • Support Vector Machines
    • Text Clustering - K-means
  • Using NLTK with other Python Libraries
    • NumPy
    • SciPy
    • Pandas
    • Matplotlib
  • Text Mining at Scale


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