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NLP – Natural Language Processing with Python


Welcome to the best natural language processing course on the Internet! This course aims to be a complete online resource for you to learn how to use natural language processing with the Python programming language.

In the course, we will cover everything you need to learn in order to use Python to become a world-class NLP practitioner.

We will start with the basics, learn how to use Python to open and process text and PDF files, and learn how to use regular expressions to search for custom patterns in text files.

After that, we will start with the basics of natural language processing, using Python’s natural language toolkit library, and the most advanced Spacy library for ultra-fast tokenization, parsing, entity recognition, and text morphological restoration.

We will understand basic NLP concepts, such as stemming, lemmatization, stop words, phrase matching, tokenization, and more!

Next, we will introduce part-of-speech tagging. Your Python script will be able to automatically assign words in the text to corresponding parts of speech, such as nouns, verbs, and adjectives. This is an important part of building an intelligent language system.

We will also learn about named entity recognition, allowing your code to automatically understand concepts such as money, time, company, and products by providing text information.

With the most advanced visualization library, we will be able to view these relationships in real time.

Then we will continue to use Scikit-Learn to understand machine learning for text classification, such as automatically building a machine learning system that can determine positive and negative movie reviews, or spam and legitimate emails.

We will extend this knowledge to more complex unsupervised learning methods for natural language processing, such as topic modeling. Our machine learning model will detect topics and main concepts from raw text files.

This course even covers advanced topics, such as using the NLTK library for sentiment analysis of text, and using the Word2Vec algorithm to create semantic word vectors.

This course contains a whole section dedicated to the most advanced advanced topics, such as using deep learning to build our own chatbots!

Not only can you get excellent technical content through this course, but you can also access our course-related Q&A forums and our real-time student chat channel, so you can collaborate with other students on projects, or get the Help on course content.

All of these come with a 30-day money-back guarantee, so you can try the course without risk.

what are you waiting for? Become an expert in natural language processing today!

This course is suitable for:

  • Python developers interested in learning how to use natural language processing.

What you will learn

  • Learn to use Python to process text files
  • Learn how to process PDF files in Python
  • Use regular expressions to search for patterns in text
  • Ultra-fast tokenization with Spacy
  • Understand stemming and lemmatization
  • Use Spacy to understand vocabulary matching
  • Use part-of-speech tags to automatically process raw text files
  • Understanding named entity recognition
  • Visualize POS and NER with Spacy
  • Use SciKit-Learn for text classification
  • Use potential Dirichlet assignments for topic modeling
  • Understanding non-negative matrix factorization
  • Use Word2Vec algorithm
  • Sentiment analysis using NLTK
  • Use deep learning to build your own chatbot


  • Course Length: 11.5 Hours
  • Course Size: 4.47 Go
  • Number of sections: 8

How to Get the Course

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