Natural Language Processing | Comprehensive NLP

Master the art of Natural Language Processing with our hands-on, developer-focused deep learning course and gain practical skills to enhance your Python programming capabilities for cutting-edge NLP applications.

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Essential Skills Gained

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Understand the fundamentals of natural language processing and deep learning.

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Implement hands-on NLP projects using standard Python libraries.

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Develop deep learning models for various NLP tasks such as sentiment analysis and text generation.

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Prepare and clean text data effectively for machine learning applications.

Format

  • Instructor-led
  • 4 days with lectures and hands-on labs.

Audience

  • Experienced Developers
  • Python Programmers
  • Data Scientists
  • Machine Learning Engineers

Description

Deep learning methods are achieving state-of-the-art results on challenging machine learning problems, such as describing photos and translating text from one language to another. Introduction to Natural Language Processing (NLP) is a highly-focused, hands-on deep learning course - written by developers, for developers – that cuts through the excess math, research papers and patchwork descriptions about natural language processing to deep dive into the technology in a meaningful, practical way to gain real world skills to leverage on the job right after the training ends. Working in a hands-on learning environment led by our expert Deep Learning practitioner, using clear explanations and standard Python libraries, students will explore step-by-step 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.

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Upcoming Course Dates

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Course Outline

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Foundations

  1. Natural Language Processing

  2. Deep Learning

  3. Promise of Deep Learning for Natural Language

  4. How to Develop Deep Learning Models With Keras

Data Preparation

  1. How to Clean Text Manually and with NLTK

  2. How to Prepare Text Data with scikit-learn

  3. How to Prepare Text Data With Keras

Bag-of-Words

  1. The Bag-of-Words Model

  2. Prepare Movie Review Data for Sentiment Analysis

  3. Neural Bag-of-Words Model for Sentiment Analysis

Word Embeddings

  1. The Word Embedding Model

  2. How to Develop Word Embeddings with Gensim

  3. How to Learn and Load Word Embeddings in Keras

Text Classification

  1. Neural Models for Document Classification

  2. Develop an Embedding + CNN Model

  3. Develop an n-gram CNN Model for Sentiment Analysis

Language Modeling

  1. Neural Language Modeling

  2. Develop a Character-Based Neural Language Model

  3. How to Develop a Word-Based Neural Language Model

  4. Develop a Neural Language Model for Text Generation

Image Captioning

  1. Neural Image Caption Generation

  2. Neural Network Models for Caption Generation

  3. Load and Use a Pre-Trained Object Recognition Model

  4. How to Evaluate Generated Text With the BLEU Score

  5. How to Prepare a Photo Caption Dataset For Modeling

  6. Develop a Neural Image Caption Generation Model

Neural Machine Translation

  1. Neural Machine Translation

  2. Encoder-Decoder Models for NMT

  3. Configure Encoder-Decoder Models for NMT

  4. How to Develop a Neural Machine Translation Model

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