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Introduction to Natural Language Processing (NLP)

Learn about Natural Language Processing and how to develop models for your projects.

Deep learning methods are achieving state-of-the-art results to challenging machine learning problems, such as identifying and describing photos and translating text from one language to another. In this highly-focused course, we’ll cut through the excess math, research papers, and patchwork descriptions about natural language processing (NLP) to dive deep into the technology so you gain real-world skills you can immediately leverage on the job.

Working in a hands-on learning environment led by our expert Deep Learning practitioner, using clear explanations and standard Python libraries, you will explore a step-by-step of what natural language processing is, the promise of deep learning in the field, how to clean and prepare text data for modeling, and how to develop deep learning models for your own natural language processing projects.

GK# 100692 Vendor# TTML5903
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Who Should Attend?

Experienced Developers, Data Scientist, Data Engineer, and others who seek to work with Natural Language Processing.

What You'll Learn

Join an engaging hands-on learning environment, where you’ll explore:

  • Neural Text Classification: Develop a deep learning model to classify the sentiment of movie reviews as either positive or negative.
  • Neural Language Modeling: Develop a neural language model on the text of Plato in order to generate new tracts of text with the same style and flavor as the original.
  • Neural Photo Captioning: Develop a model to automatically generate a concise description of ad hoc photographs.
  • Neural Machine Translation: Develop a model to translate sentences of text in German to English.
  • Neural Bag-of-Words: Develop neural network models that model text as a bag-of-words where word order is ignored.
  • Neural Word Embedding: Develop neural network models that model text using a distributed representation.
  • Embedding + CNN: Develop deep learning models that combine word embedding representations with convolutional neural networks.
  • Encoder-Decoder RNN: Develop recurrent neural networks that use the encoder-decoder architecture.

This course has a 50% hands-on labs to 50% lecture ratio with engaging instruction, demos, group discussions, labs, and project work.


Course Outline


  • Natural Language Processing
  • Deep Learning
  • Promise of Deep Learning for Natural Language
  • How to Develop Deep Learning Models With Keras

Data Preparation

  • How to Clean Text Manually and with NLTK
  • How to Prepare Text Data with scikit-learn
  • How to Prepare Text Data With Keras


  • The Bag-of-Words Model
  • Prepare Movie Review Data for Sentiment Analysis
  • Neural Bag-of-Words Model for Sentiment Analysis

Word Embeddings

  • The Word Embedding Model
  • How to Develop Word Embeddings with Gensim
  • How to Learn and Load Word Embeddings in Keras

Text Classification

  • Neural Models for Document Classification
  • Develop an Embedding + CNN Model
  • Develop an n-gram CNN Model for Sentiment Analysis

Language Modeling

  • Neural Language Modeling
  • Develop a Character-Based Neural Language Model
  • How to Develop a Word-Based Neural Language Model
  • Develop a Neural Language Model for Text Generation

Image Captioning

  • Neural Image Caption Generation
  • Neural Network Models for Caption Generation
  • Load and Use a Pre-Trained Object Recognition Model
  • How to Evaluate Generated Text With the BLEU Score
  • How to Prepare a Photo Caption Dataset For Modeling
  • Develop a Neural Image Caption Generation Model

Neural Machine Translation

  • Neural Machine Translation
  • Encoder-Decoder Models for NMT
  • Configure Encoder-Decoder Models for NMT
  • How to Develop a Neural Machine Translation Model


Before attending this course, you should have:

  • Strong Python skills
  • Prior working experience with Keras is useful
  • Ability to navigate the Linux command line
  • Basic knowledge of Linux editors (such as VI/nano) for editing code