Natural Language Processing

Learn to conduct Natural Language Processing with Python. You’ll learn the latest techniques in semantic analysis, topic modeling, and more. Enroll today!

 

Student rating

5.00 Out Of 47 Students

What You’ll Learn

  • 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

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Course Content

This course is designed to be your complete online resource for learning how to use Natural Language Processing with the Python programming language. You’ll learn the basics of how to open and work with text and PDF files with Python and utilize the Natural Language Toolkit library for Python, as well as the state of the art Spacy library for ultra fast tokenization, parsing, entity recognition, and lemmatization of text. The course will cover Part-of-Speech tagging, named entity recognition, text classification with Scikit-Learn, and expand your learning into more complex unsupervised learning methods for natural language processing, such as topic modelling, where our machine learning models will detect topics and major concepts from raw text files. You’ll also have the opportunity to learn advanced topics, such as sentiment analysis of text with the NLTK library, semantic word vectors with the Word2Vec algorithm, and how to use deep learning to build out your own chat bots!

 

 

  • 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

 

 

Experienced Python developers who want to learn to use Python for working with natural language text data.

 

 

Course Description

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!

 

What Students Are Saying

Well laid out material, great examples. Overall great course

Hamish Campbell

Brilliant Course, Very sound explanations and very easy to follow along.

Joshua Lembong

Yes, so far it has been teaching many things I indeed needed.

Niccolo' Gentile

The course content is good for NLP Beginner. About the teaching, @Jose... you are a great teacher. Thanks for the step-by-step guide.

Bayode Ogunleye

Awesome insight into nlp and what it can be used for. I personally work with software robots so these types of techniques will be coming handy :) onward to the Python CV course!

Ville-Pekka Palmgren

This course widely covers NLP topics and explained very well. The codes are practical and useful. I definitely satisfy the content and recommend to everyone who is interested in the NLP.

Takashi Yoneya

I highly recommend the course! It's been easy to follow even without any previous experience in Python. I love how he goes beyond just the utilization of the library and actually explains some of the science behind the libraries.

Kervin Vasquez

The teacher is fantastic and I thoroughly enjoyed the course. So much so that I decided to purchase another course. I appreciate the assessments that come with each chapter as they help the material sink in.

Marko Maric

Great course with well-planned videos and content. The 'assessment' notebooks at the end of each module are good -- they allow you to practice the material you learned in each section.

Michael Zalewski

This is an awesome course on getting familiar with the tools associated with NLP.

Blaine Edwards
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Course type
  • Featured
  • In-House
  • Self-Paced
Student rating

5.00

Course length

80 Lectures

18 Hours

On-Demand training
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