Hands-on-NLP-with-NLTK-and-scikit-learn-

Hands-on NLP with NLTK and scikit-learn[video], published by Packt

Hands-on NLP with NLTK and Scikit-learn [Video]

This is the code repository for Hands-on NLP with NLTK and Scikit-learn [Video], published by Packt. It contains all the supporting project files necessary to work through the video course from start to finish.

About the Video Course

Your colleagues depend on you to monetize gigabytes of unstructured text data. What do you do? Taking this course will help you to precisely create new applications with Python and NLP. You will be able to build actual solutions backed by machine learning and NLP processing models with ease.

There is an overflow of text data online nowadays. As a Python developer, you need to create a new solution using Natural Language Processing for your next project. Your colleagues depend on you to monetize gigabytes of unstructured text data. What do you do? Hands-on NLP with NLTK and scikit-learn is the answer. This course puts you right on the spot, starting off with building a spam classifier in our first video. At the end of the course, you are going to walk away with three NLP applications: a spam filter, a topic classifier, and a sentiment analyzer. There is no need for fancy mathematical theory, just plain English explanations of core NLP concepts and how to apply those using Python libraries. Taking this course will help you to precisely create new applications with Python and NLP. You will be able to build actual solutions backed by machine learning and NLP processing models with ease.

What You Will Learn

Build end-to-end Natural Language Processing solutions, ranging from getting data for your model to presenting its results.
Core NLP concepts such as tokenization, stemming, and stop word removal.
Use open source libraries such as NLTK, scikit-learn, and spaCy to perform routine NLP tasks.
Classify emails as spam or not-spam using basic NLP techniques and simple machine learning models.
Put documents in their relevant topics using techniques such as TF-IDF, SVMs, and LDAs.
Common text data processing steps to increase the performance of your machine learning models.

Instructions and Navigation

Assumed Knowledge

To fully benefit from the coverage included in this course, you will need:
This course is for developers, data scientists, and programmers who want to learn about practical Natural Language Processing with Python in a hands-on way. Developers who have an upcoming project that needs NLP, or a pile of unstructured text data on their hands, and don't know what to do with it, will find this course useful. Prior programming experience with Python is assumed along with being comfortable dealing with machine learning terms such as supervised learning, regression, and classification. No prior Natural Language Processing or text mining experience is needed.

Technical Requirements

This course has the following software requirements:
SETUP AND INSTALLATION Minimum Hardware Requirements For successful completion of this course, students will require the computer systems with at least the following:

OS: Windows 7 SP1 64-bit, Windows 8.1 64-bit or Windows 10 64-bit

Processor: Intel Core i5 or equivalent

Memory: 8 GB RAM

Storage: 35 GB available space

Recommended Hardware Requirements For an optimal experience with hands-on labs and other practical activities, we recommend the following configuration:

OS: Windows 7 SP1 64-bit, Windows 8.1 64-bit or Windows 10 64-bit

Processor: Intel Core i7 or equivalent

Memory: 16 GB RAM

Storage: 35 GB available space

Software Requirements

OS: Windows 7 or Windows 10

Browser: Google Chrome, Latest Version

Code Editor: Atom IDE, Latest Version

Others: Python3 installed using the Anaconda package or equivalent, Tensorflow r1.4

Exercise Files

Exercise files should have a start and an end state for each video that contains a demonstration of code.

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