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What you'll learn
- Overview of NLP
- Understand and use techniques from NLP
- Learn to work with Text Files with Python
- Use NLTK for Sentiment Analysis
- Write your own sentiment analysis code in Python
- Introduction to some key techniques from NLP
- Write your own spam detection code in Python
course Description :
Welcome to the best Natural Language Processing course on the Udemy! This course is designed to be your complete online resource for learning 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 become a world class practitioner of NLP with Python.
We'll start off with the basics, learning how to open and work with text, as well as learning how to use regular expressions to search for custom patterns inside of text files.
Afterwards we will begin with the basics of Natural Language Processing, utilizing 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.
We'll understand fundamental NLP concepts such as stemming, lemmatization, stop words, tokenization and more!
Next we will cover Part-of-Speech tagging, where your Python scripts will be able to automatically assign words in text to their appropriate part of speech, such as nouns, verbs and adjectives, an essential part of building intelligent language systems.
We'll also learn about named entity recognition, allowing your code to automatically understand concepts like money, time, companies, products, and more simply by supplying the text information.
Through state of the art visualization libraries we will be able view these relationships in real time.
Then we will move on to understanding machine learning with Scikit-Learn to conduct text classification, such as automatically building machine learning systems that can determine positive versus negative movie reviews, or spam versus legitimate email messages.
We will expand this knowledge to 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.
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