David Snyder – Real World NLP
David Snyder – Real World NLP| 3.05 GB
This is a comprehensive NLP certification program led by David Snyder that focuses on his approach to Neuro Linguistic Programming. It is a recording of a Practitioner Certification seminar that you can use to master all of the elements of NLP in a practical and real world setting.
Welcome to the best Natural Language Processing course on the internet! 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 and PDF files with Python, 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 L
anguage 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, phrase matching, 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.
This course even covers advanced topics, such as sentiment analysis of text with the
NLTK library, and creating semantic word vectors with the Word2Vec algorithm.
Included in this course is an entire section devoted to state of the art advanced topics, such as using deep learning to build out our own chat bots!
