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Description

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Overview:

In this Natural Language Processing course, you will learn how to navigate the various text pre-processing techniques and select the best neural network architecture for Natural Language Processing.

Outline:

Module 1: Introduction to Natural Language Processing

In this module, you will learn about:

  • The basics of Natural Language Processing and its applications
  • Popular text pre-processing techniques
  • Word2vec and Glove word embeddings Sentiment classification

Module 2: Applications of Natural Language Processing

In this module, you will learn about:

  • Named Entity Recognition and how to develop it using popular libraries
  • Parts of Speech Tagging

Module 3: Introduction to Neural Networks

In this module, you will learn about:

  • Basics of Gradient descent and backpropagation.
  • Fundamentals of Deep Learning, Keras and deploying a Model-as-a-Service (MaaS)

Module 4: Foundations of Convolutional Neural Networks (CNN)

  • In this module, you will learn about CNN architecture, application areas, and implementation using Keras.

Module 5: Recurrent Neural Networks (RNN)

  • In this module, you will learn about RNN architecture, application areas, vanishing gradients, and implementation using Keras.

Module 6: Gated Recurrent Units (GRU)

  • In this module, you will learn about GRU architecture, application areas, and implementation using Keras.

Module 7: Long Short-Term Memory (LSTM)

  • In this module, you will learn about LSTM architecture, application areas, and implementation using Keras.

Module 8: State of the Art in Natural Language Processing

In this module, you will learn how to:

  • Perform Attention Model and Beam search
  • Use End to End models for speech processing
  • Use Dynamic Neural Networks to answer questions

Module 9: A Practical NLP Project Workflow in an Organization

In this module, you will learn how to:

  • Acquire data using free datasets and crowdsourcing
  • Use cloud infrastructure, such as the Google collab notebook, to train deep learning NLP models
  • Write a Flask framework server RestAPI to deploy a model
  • Deploy the web service on cloud infrastructures such as Amazon Elastic Compute Cloud (Amazon EC2) or Docker Cloud
  • Leverage the promising techniques in NLP, such as Bidirectional Encoder Representations from Transformers (BERT)

Additional information

Length

3 days

Guaranteed to run

No