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LinkedIn Learning

Advance Your Skills in Natural Language Processing

via LinkedIn Learning Path

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Overview

Natural language processing is quickly emerging as a key skill for machine learning engineers. This advanced-level learning path provides machine learning engineers with advanced NLP techniques and tools to further their knowledge in this industry-defining field.
  • Translate text data into powerful insights using Python.
  • Learn about transformers, the go-to architecture of NLP.
  • Build NLP apps with transformers.

Syllabus

Courses under this program:
Course 1: Advanced NLP with Python for Machine Learning (2020)
-Build upon your foundational knowledge of natural language processing (NLP) by exploring more complex topics such as word2vec, doc2vec, and recurrent neural networks.

Course 2: Hands-On Natural Language Processing
-Learn to use natural language processing to make sense of text data and derive useful insights.

Course 3: Building NLP Pipelines with spaCy
-Learn the essentials of problem-solving with spaCy, the popular, open-source software library for advanced natural language processing.

Course 4: Deep Learning Foundations: Natural Language Processing with TensorFlow
-Learn foundational deep learning techniques to classify, predict, and generate text using different neural networks.

Course 5: Recurrent Neural Networks
-Learn the basics of recurrent neural networks to get up and running with RNN quickly.

Course 6: Generative AI: Working with Large Language Models
-Explore a user-friendly approach to working with transformers and large language models for natural language processing.

Courses

Taught by

Derek Jedamski, Wuraola Oyewusi, Prateek Sawhney, Harshit Tyagi, Kumaran Ponnambalam and Jonathan A. Fernandes

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