Introduction to Natural Language Processing

3 tutorials

In this module, we build on your machine learning knowledge and zoom into natural language processing (NLP). NLP focuses on developing computer algorithms and models that can understand, interpret and generate natural language.

NLP has become increasingly important in recent years due to the rapid growth of digital content, including text, speech and video data. With the help of NLP, computers can process and analyse large amounts of text data.

Note that this module assumes you have some background in Python, EDA and basic machine learning. We will also be making use of Jupyter notebook. If these are completely new to you, please go through the following on StackUp Learn first before starting this module:

Helpful prior knowledge


Learning Outcomes

By the end of this module, you will be able to:

  • Understand the concept of NLP and how its real-world applications
  • Apply the fundamental text preprocessing steps used for NLP
  • Implemented several vectorization techniques for text data
  • Understand the differences between several vectorization techniques
  • Used scikit-learn and nltk to solve NLP problems using classification
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