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.
Natural Language Processing Course Delivery Methods
Natural Language Processing Course Benefits
Understand various pre-processing techniques for deep learning problems
Build a vector representation of text using word2vec and GloVe
Create a named entity recogniser and parts-of-speech tagger with Apache OpenNLP
Build a machine translation model in Keras, a deep learning API
Develop a text generation application using Long short-term memory (LSTM)
Build a trigger word detection application using an attention model
Test your knowledge in the included end-of-course exam
Continue learning and face new challenges with after-course one-on-one instructor coaching
Natural Language Processing Course Outline
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
In this module, you will learn about:
- Named Entity Recognition and how to develop it using popular libraries
- Parts of Speech Tagging
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)
- In this module, you will learn about CNN architecture, application areas, and implementation using Keras.
- In this module, you will learn about RNN architecture, application areas, vanishing gradients, and implementation using Keras.
- In this module, you will learn about GRU architecture, application areas, and implementation using Keras.
- In this module, you will learn about LSTM architecture, application areas, and implementation using Keras.
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
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)
