This course will give the participant a deep dive into the natural language processing (NLP) technology. Using standard Python libraries the attendee will explore step-by-step what natural language processing is, how to clean and prepare text data for modeling, and how to develop deep learning models for natural language processing projects.

3 days - $1,895.00

Course taught by an expert Python coder.

Prerequisites:

Strong Python skills are required.  Ability to navigate the Linux command line and basic knowledge of Linux editors (such as VI/nano) for editing code are essential. Prior working experience with Keras is useful too.

Course Outline 

Foundations
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

Bag-of-Words
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