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Applied ML: Build NLP text embeddings using python

One of the most important techniques in Natural Language Processing to represent text as meaningful numerical vectors

Description

Natural Language Processing (NLP) is a subfield of Artificial Intelligence and Machine Learning where we work with unstructured text data - human or machine-generated. If you are new to AI and ML space and would like to know where exactly NLP fits in the bigger picture, I would like to suggest the course "Applied ML: The Big Picture"

But once you've arrived here interested in NLP, I'd like to say you've taken the right step in knowing more about this interesting and challenging field. The language we speak is rich in information across several dimensions, and even realizing these dimensions is a research exercise. For this reason, NLP data is one of the most exciting ones to work with while developing ML models.

Embeddings are just techniques that attempt to decipher some of these dimensions and put them into a numerical format. It's the first and most crucial step before getting into advanced NLP algorithms and tasks such as machine translation, chatbot development, etc.

This course provides the learner with the foundational concepts along with two coding exercises, with attached Jupiter notebooks, to provide a practical experience on the purpose and usefulness of text embeddings. Hopefully, this inspires and prepares the learner to explore more topics in the exciting field of NLP.

Goals

  • Semantics and context in text data
  • Why it's important to embed texts
  • Embedding libraries in python
  • How to find text similarity

Prerequisites

  • Awareness of Natural Language Processing

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Curriculum

  • Introduction
    01:15
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Applied ML: Build NLP text embeddings using python
This Course Includes
  • 36 mins
  • 8 Lectures
  • Completion Certificate
  • Lifetime Access
  • 30-Days Money Back Guarantee

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