NLP - Natural Language Processing with Python at UDEMY Overview
Duration | 12 hours |
Total fee | ₹499 |
Mode of learning | Online |
Difficulty level | Intermediate |
Official Website | Go to Website  |
Credential | Certificate |
- Overview
- Highlights
- Course Details
- Curriculum
- Student Reviews
NLP - Natural Language Processing with Python at UDEMY Highlights
- Compatible on Mobile and TV
- Earn a Cerificate on successful completion
- Get Full Lifetime Access
- Course Instructor
- Jose Portilla
NLP - Natural Language Processing with Python at UDEMY Course details
- Python developers interested in learning how to use Natural Language Processing.
- 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
- Use the Word2Vec algorithm
- Use NLTK for Sentiment Analysis
- Use Deep Learning to build out your own chat bot
- 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 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 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! 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 garuantee, 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
NLP - Natural Language Processing with Python at UDEMY Curriculum
Introduction
Course Overview - DO NOT SKIP THIS LECTURE PLEASE. IMPORTANT INFO HERE!
Curriculum Overview
Installation and Setup Lecture
FAQ - Frequently Asked Questions
Python Text Basics
Introduction to Python Text Basics
Working with Text Files with Python - Part One
Working with Text Files with Python - Part Two
Working with PDFs
Regular Expressions Part One
Regular Expressions Part Two
Python Text Basics - Assessment Overview
Python Text Basics - Assessment Solutions
Natural Language Processing Basics
Introduction to Natural Language Processing
Spacy Setup and Overview
What is Natural Language Processing?
Spacy Basics
Tokenization - Part One
Tokenization - Part Two
Stemming
Lemmatization
Stop Words
Phrase Matching and Vocabulary - Part One
Phrase Matching and Vocabulary - Part Two
NLP Basics Assessment Overview
NLP Basics Assessment Solution
Part of Speech Tagging and Named Entity Recognition
Introduction to Section on POS and NER
Part of Speech Tagging
Visualizing Part of Speech
Named Entity Recognition - Part One
Named Entity Recognition - Part Two
Visualizing Named Entity Recognition
Sentence Segmentation
Part Of Speech Assessment
Part Of Speech Assessment - Solutions
Text Classification
Introduction to Text Classification
Machine Learning Overview
Classification Metrics
Confusion Matrix
Scikit-Learn Primer - How to Use SciKit-Learn
Scikit-Learn Primer - Code Along Part One
Scikit-Learn Primer - Code Along Part Two
Text Feature Extraction Overview
Text Feature Extraction - Code Along Implementations
Text Feature Extraction - Code Along - Part Two
Text Classification Code Along Project
Text Classification Assessment Overview
Text Classification Assessment Solutions
Semantics and Sentiment Analysis
Introduction to Semantics and Sentiment Analysis
Overview of Semantics and Word Vectors
Semantics and Word Vectors with Spacy
Sentiment Analysis Overview
Sentiment Analysis with NLTK
Sentiment Analysis Code Along Movie Review Project
Sentiment Analysis Project Assessment
Sentiment Analysis Project Assessment - Solutions
Topic Modeling
Introduction to Topic Modeling Section
Overview of Topic Modeling
Latent Dirichlet Allocation Overview
Latent Dirichlet Allocation with Python - Part One
Latent Dirichlet Allocation with Python - Part Two
Non-negative Matrix Factorization Overview
Non-negative Matrix Factorization with Python
Topic Modeling Project - Overview
Topic Modeling Project - Solutions
Deep Learning for NLP
Introduction to Deep Learning for NLP
The Basic Perceptron Model
Introduction to Neural Networks
Keras Basics - Part One
Keras Basics - Part Two
Recurrent Neural Network Overview
LSTMs, GRU, and Text Generation
Text Generation with LSTMs with Keras and Python - Part One
Text Generation with LSTMs with Keras and Python - Part Two
Text Generation with LSTMS with Keras - Part Three
Chat Bots Overview
Creating Chat Bots with Python - Part One
Creating Chat Bots with Python - Part Two
Creating Chat Bots with Python - Part Three
Creating Chat Bots with Python - Part Four
BONUS SECTION: THANK YOU!
BONUS LECTURE
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