Description
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)
