NLP-Natural Language Processing in Python
NLP-Natural Language Processing in Python
Language,Python,Development,Data Science,Machine Learning
Lectures -234
Resources -3
Duration -23.5 hours
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Course Description
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\u00a0PDF files with Python, as well as learning how to use regular expressions to search for custom patterns inside text files.
Afterward, 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, 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 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 modeling, 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\u00a0library, 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 chatbots!
Not only do you get fantastic technical content with this course, but you will also get access to both our course-related Question and Answer forums, as well as our live student chat channel, so you can team up with other students for projects, or get help on the course content from myself and the course teaching assistants.
All of this comes with a 30-day money-back guarantee, so you can try the course risk-free.
What are you waiting for? Become an expert in natural language processing today!
I will see you inside the course,
Jose
Goals
Learn to work with Text Files with Python
Learn how to work with PDF files in Python
Utilize Regular Expressions for pattern searching in text
Use Spacy for ultra fast tokenization
Learn about Stemming and Lemmatization
Understand Vocabulary Matching with Spacy
Use Part of Speech Tagging to automatically process raw text files
Understand Named Entity Recognition
Visualize POS and NER with Spacy
Use SciKit-Learn for Text Classification
Use Latent Dirichlet Allocation for Topic Modelling
Learn about Non-negative Matrix Factorization
Prerequisites
Understand general Python
Curriculum
Check out the detailed breakdown of what’s inside the course
Introduction
7 Lectures
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Promo 01:55 01:55
-
Introduction to Course 00:55 00:55
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Introduction to Instructor 05:44 05:44
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Introduction to Co-Instructor 01:30 01:30
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Course Introduction 11:16 11:16
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Introduction To Instructor New 02:19 02:19
-
Resources
Introduction(Regular Expressions)
4 Lectures
![Tutorialspoint](/market/public/assets/images/loader.gif)
Meta Characters(Regular Expressions)
25 Lectures
![Tutorialspoint](/market/public/assets/images/loader.gif)
Pattern Objects(Regular Expressions)
6 Lectures
![Tutorialspoint](/market/public/assets/images/loader.gif)
More Meta Characters(Regular Expressions)
3 Lectures
![Tutorialspoint](/market/public/assets/images/loader.gif)
String Modification(Regular Expressions)
4 Lectures
![Tutorialspoint](/market/public/assets/images/loader.gif)
Words and Tokens(Text Preprocessing)
5 Lectures
![Tutorialspoint](/market/public/assets/images/loader.gif)
Sentiment Classification(Text Preprocessing)
12 Lectures
![Tutorialspoint](/market/public/assets/images/loader.gif)
Language Independent Tokenization(Text Preprocessing)
11 Lectures
![Tutorialspoint](/market/public/assets/images/loader.gif)
Text Nomalization(Text Preprocessing)
4 Lectures
![Tutorialspoint](/market/public/assets/images/loader.gif)
String Matching and Spelling Correction(Text Preprocessing)
8 Lectures
![Tutorialspoint](/market/public/assets/images/loader.gif)
Language Modeling
10 Lectures
![Tutorialspoint](/market/public/assets/images/loader.gif)
Topic Modelling with Word and Document Representations
16 Lectures
![Tutorialspoint](/market/public/assets/images/loader.gif)
Word Embeddings LSI
12 Lectures
![Tutorialspoint](/market/public/assets/images/loader.gif)
Word Semantics
13 Lectures
![Tutorialspoint](/market/public/assets/images/loader.gif)
Word2vec(Optional)
13 Lectures
![Tutorialspoint](/market/public/assets/images/loader.gif)
Need of Deep Learning for NLP(NLP with Deep Learning DNN)
3 Lectures
![Tutorialspoint](/market/public/assets/images/loader.gif)
Introduction(NLP with Deep Learning DNN)
11 Lectures
![Tutorialspoint](/market/public/assets/images/loader.gif)
Training(NLP with Deep Learning DNN)
9 Lectures
![Tutorialspoint](/market/public/assets/images/loader.gif)
Hyper parameters(NLP with Deep Learning DNN)
10 Lectures
![Tutorialspoint](/market/public/assets/images/loader.gif)
Introduction(NLP with Deep Learning RNN)
7 Lectures
![Tutorialspoint](/market/public/assets/images/loader.gif)
Mini-project Language Modelling(NLP with Deep Learning RNN)
10 Lectures
![Tutorialspoint](/market/public/assets/images/loader.gif)
Mini-project Sentiment Classification(NLP with Deep Learning RNN)
6 Lectures
![Tutorialspoint](/market/public/assets/images/loader.gif)
RNN in PyTorch(NLP with Deep Learning RNN)
10 Lectures
![Tutorialspoint](/market/public/assets/images/loader.gif)
Advanced RNN models(NLP with Deep Learning RNN)
2 Lectures
![Tutorialspoint](/market/public/assets/images/loader.gif)
Neural Machine Translation
13 Lectures
![Tutorialspoint](/market/public/assets/images/loader.gif)
Instructor Details
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