This is the code repository for Next Generation Natural Language Processing with Python [Video], published by Packt. It contains all the supporting project files necessary to work through the video course from start to finish.
The company you work for has accumulated a lot of valuable data from its customers, all stored as text, and you need to extract some value from that data. You’ve spent a lot of combined time writing about what they want but no-one knows what they have written about and no-one has the time to read all the messages.
This course empowers you to know how to attack this and other text analysis problems to unlock value for your organization. You’ll start by seeing how NLP can help you extract useful information from large collections of text data, and how you can use the latest Python libraries for NLP. Then we’ll show you how to solve a practical problem using NLP by building a spam SMS detector. You’ll also learn to convert words into numbers that can be analyzed.
Moving on, we’ll teach you how to accurately label new documents to get an accuracy score and cluster your data together. Finally, you’ll see more advanced analysis and will model text by using vector space models and semantic parsing to break down the components of a sentence. You’ll also work with neural networks and learn how to write believable text.
By the end of the course, you’ll be able to use the latest libraries of NLP in Python in your day-to-day tasks. All the code and supporting files for this course are available on Github at https://github.com/PacktPublishing/Next-Generation-Natural-Language-Processing-with-Python
- Explore the NLP techniques and understand what they can do to help you
- Turn text into numbers using the new NLP libraries in Python
- Perform different techniques to categorize text data
- Cluster similar text documents together using Gensim
- Accurately label new documents to get an accuracy score
- Extract meaning and insights from text data such as vector space models
- Use semantic parsing to break down the components of a sentence
To fully benefit from the coverage included in this course, you will need:
The course is for Python developers, researchers, and data scientists who are familiar with analysis of different kinds of data but can’t easily transfer their understanding of numerical data to textual data. Knowledge of machine learning and data analysis is required.
Recommended Hardware Requirements
For an optimal experience with hands-on labs and other practical activities, we recommend the following configuration:
- Operating system: Mac/Linux
- Processor: 2.0GHz quad core
- Memory: 16GB
- Storage: 4GB
Software Requirements
- Python Anaconda installation
- Jupyter notebooks
- Spacy and textblob installed using conda