Home
Courses
Leveraging Deep Learning for Natural Language Processing Course

Leveraging Deep Learning for Natural Language Processing Course

Course 1278

Duration: 3 days
Labs: Yes
Language: English
Level: Intermediate

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

In-Person
Online

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 recognizer 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

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)

Get This Course $2,650

3-day instructor led training course
After-course coaching available

#1278

Sep 26 - 28 9:00 AM - 4:30 PM EDT

New York or Virtual
Nov 20 - 22 9:00 AM - 4:30 PM EST

Ottawa or Virtual
Mar 26 - 28 9:00 AM - 4:30 PM EDT

New York or Virtual
May 22 - 24 9:00 AM - 4:30 PM EDT

Ottawa or Virtual

3-day instructor led training course
After-course coaching available

#1278

Sep 26 - 28 9:00 AM - 4:30 PM EDT

New York or Virtual
Nov 20 - 22 9:00 AM - 4:30 PM EST

Ottawa or Virtual
Mar 26 - 28 9:00 AM - 4:30 PM EDT

New York or Virtual
May 22 - 24 9:00 AM - 4:30 PM EDT

Ottawa or Virtual

Bring this or any training to your organization
Full - scale program development
Delivered when, where, and how you want it
Blended learning models
Tailored content
Expert team coaching

#1278

Questions about this course?

Customize Your Team Training Experience

Fill out the form below or call 888-843-8733

* Required Fields

Preferred method of contact:

Phone

Need Help Finding The Right Training Solution?

Our training advisors are here for you.

Natural Language Processing Training FAQs

To succeed in this course, you must have a strong working knowledge of Introduction to Python Training. We also recommend some knowledge of linear algebra and machine learning.

You will get a better understanding of the algorithms used in natural language processing if you first do one of the following:

More and more, the source of data for AI is in the form of human text or voice. With this course, you will gain the necessary skills to transform this data into numbers that can be used to gain insights from such data. This would be for understanding, for instance, chatbots or sentiment analysis, classification of documents, search, etc.

Yes! Our Leveraging Deep Learning for Natural Language Processing course builds on these courses and uses Jupyter and Keras to build models for natural language processing.

Leveraging Deep Learning for Natural Language Processing Course

Natural Language Processing Course Delivery Methods

Natural Language Processing Course Benefits

Natural Language Processing Course Outline

Module 1: Introduction to Natural Language Processing

Module 2: Applications of Natural Language Processing

Module 3: Introduction to Neural Networks

Module 4: Foundations of Convolutional Neural Networks (CNN)

Module 5: Recurrent Neural Networks (RNN)

Module 6: Gated Recurrent Units (GRU)

Module 7: Long Short-Term Memory (LSTM)

Module 8: State of the Art in Natural Language Processing

Module 9: A Practical NLP Project Workflow in an Organization

Natural Language Processing Training FAQs

What are the prerequisites for this Natural Language Processing Course?

I have already taken Introduction to Python Training. Will I be prepared for this course?

I am a Data Science Analyst, Python Data Science Programmer, or Quantitative Researcher. How can I benefit from this course?

I have already taken (one of) Introduction to Data Science, Machine Learning & AI using Python, Applied Data Science with Python and Jupyter, or {course:1270}. Will I still benefit from this course?

Related Courses