Natural Language Processing

Price
$1,895.00 USD

Duration
2 Days

 

Delivery Methods
Virtual Instructor Led
Private Group

Course Overview

Natural Language Processing is the use of machine learning and data analysis to build models and reveal insights based on natural text data. This course is designed for experienced Python developers who want to learn how to apply state of the art Natural Language Processing frameworks and techniques, such as the open source frameworks SpaCy and NLTK, to any kinds of natural language text data. Students taking this course will discover the latest techniques in semantic analysis, topic modeling, text classification, and more!

Private classes on this topic are available. We can address your organization’s issues, time constraints, and save you money, too. Contact us to find out how.

This course includes 6-months access to the full course content in on-demand format to support post-class reference and review.

Course Objectives

  • Learn to work with Text Files with Python
  • Learn how to work with PDF files in Python
  • Utilize Regular Expressions for pattern searching in text
  • Use Spacy for ultra fast tokenization
  • Learn about Stemming and Lemmatization
  • Understand Vocabulary Matching with Spacy
  • Use Part of Speech Tagging to automatically process raw text files
  • Understand Named Entity Recognition
  • Visualize POS and NER with Spacy
  • Use SciKit-Learn for Text Classification
  • Use Latent Dirichlet Allocation for Topic Modelling
  • Learn about Non-negative Matrix Factorization
  • Use the Word2Vec algorithm
  • Use NLTK for Sentiment Analysis
  • Use Deep Learning to build out your own chat bot

Who Should Attend?

Intermediate to experienced Python developers or experienced developers coming from another programming language.
  • Top-rated instructors: Our crew of subject matter experts have an average instructor rating of 4.8 out of 5 across thousands of reviews.
  • Authorized content: We maintain more than 35 Authorized Training Partnerships with the top players in tech, ensuring your course materials contain the most relevant and up-to date information.
  • Interactive classroom participation: Our virtual training includes live lectures, demonstrations and virtual labs that allow you to participate in discussions with your instructor and fellow classmates to get real-time feedback.
  • Post Class Resources: Review your class content, catch up on any material you may have missed or perfect your new skills with access to resources after your course is complete.
  • Private Group Training: Let our world-class instructors deliver exclusive training courses just for your employees. Our private group training is designed to promote your team’s shared growth and skill development.
  • Tailored Training Solutions: Our subject matter experts can customize the class to specifically address the unique goals of your team.

Learning Credits: Learning Credits can be purchased well in advance of your training date to avoid having to commit to specific courses or dates. Learning Credits allow you to secure your training budget for an entire year while eliminating the administrative headache of paying for individual classes. They can also be redeemed for a full year from the date of purchase. If you have previously purchased a Learning Credit agreement with New Horizons, you may use a portion of your agreement to pay for this class.

If you have questions about Learning Credits, please contact your Account Manager.

Agenda

Handling Text with Python

  • Working with PDFs

Regular Expressions

Natural Language Processing

  • Tokenization
  • Stemming
  • Lemmatization

Part of Speech Tagging

  • Named Entity Recognition
  • Sentence Segmentation

Text Classification

Semantics and Sentiment Analysis

Topic Modeling

SpaCy

NLTK

Deep Learning for Natural Language Processing

 

Get in touch to schedule training for your team
We can enroll multiple students in an upcoming class or schedule a dedicated private training event designed to meet your organization’s needs.

 



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