NLP - Natural Language Processing with Python
4.6 (5,267 ratings)
Course Ratings are calculated from individual students’ ratings and a variety of other signals, like age of rating and reliability, to ensure that they reflect course quality fairly and accurately.
25,809 students enrolled

NLP - Natural Language Processing with Python

Learn to use Machine Learning, Spacy, NLTK, SciKit-Learn, Deep Learning, and more to conduct Natural Language Processing
Bestseller
4.6 (5,267 ratings)
Course Ratings are calculated from individual students’ ratings and a variety of other signals, like age of rating and reliability, to ensure that they reflect course quality fairly and accurately.
25,792 students enrolled
Created by Jose Portilla
Last updated 9/2019
English
English [Auto], French [Auto], 5 more
  • German [Auto]
  • Indonesian [Auto]
  • Italian [Auto]
  • Portuguese [Auto]
  • Spanish [Auto]
Current price: $139.99 Original price: $199.99 Discount: 30% off
5 hours left at this price!
30-Day Money-Back Guarantee
This course includes
  • 11.5 hours on-demand video
  • 2 articles
  • 2 downloadable resources
  • Full lifetime access
  • Access on mobile and TV
  • Certificate of Completion
Training 5 or more people?

Get your team access to 4,000+ top Udemy courses anytime, anywhere.

Try Udemy for Business
What you'll learn
  • 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
Course content
Expand all 80 lectures 11:24:48
+ Introduction
4 lectures 21:57

Please make sure to watch the Course Overview Lecture.

Thanks!

Quick Check
1 question
Curriculum Overview
03:07
Installation and Setup Lecture
11:51
FAQ - Frequently Asked Questions
02:52
+ Python Text Basics
8 lectures 01:20:00
Introduction to Python Text Basics
00:36
Working with Text Files with Python - Part One
11:00
Working with Text Files with Python - Part Two
20:39
Working with PDFs
12:35
Regular Expressions Part One
15:18
Regular Expressions Part Two
10:35
Python Text Basics - Assessment Overview
02:24
Python Text Basics - Assessment Solutions
06:53
+ Natural Language Processing Basics
13 lectures 01:44:26
Introduction to Natural Language Processing
00:41
Spacy Setup and Overview
07:23
What is Natural Language Processing?
03:00
Spacy Basics
19:09
Tokenization - Part Two
06:13
Stemming
09:23
Lemmatization
06:32
Stop Words
04:55
Phrase Matching and Vocabulary - Part One
14:27
Phrase Matching and Vocabulary - Part Two
06:42
NLP Basics Assessment Overview
02:44
NLP Basics Assessment Solution
07:40
+ Part of Speech Tagging and Named Entity Recognition
9 lectures 01:16:36
Introduction to Section on POS and NER
00:32
Part of Speech Tagging
16:50
Named Entity Recognition - Part One
09:58
Named Entity Recognition - Part Two
09:20
Sentence Segmentation
15:59
Part Of Speech Assessment
02:19
Part Of Speech Assessment - Solutions
08:22
+ Text Classification
13 lectures 01:51:19
Introduction to Text Classification
00:50
Machine Learning Overview
09:53
Classification Metrics
11:54
Confusion Matrix
09:49
Scikit-Learn Primer - How to Use SciKit-Learn
04:18
Scikit-Learn Primer - Code Along Part One
15:22
Scikit-Learn Primer - Code Along Part Two
08:46
Text Feature Extraction Overview
06:21
Text Feature Extraction - Code Along Implementations
14:29
Text Feature Extraction - Code Along - Part Two
10:59
Text Classification Code Along Project
10:56
Text Classification Assessment Overview
01:07
Text Classification Assessment Solutions
06:35
+ Semantics and Sentiment Analysis
8 lectures 01:04:19
Introduction to Semantics and Sentiment Analysis
00:27
Overview of Semantics and Word Vectors
07:30
Semantics and Word Vectors with Spacy
17:11
Sentiment Analysis Overview
04:45
Sentiment Analysis with NLTK
13:04
Sentiment Analysis Code Along Movie Review Project
07:38
Sentiment Analysis Project Assessment
02:41
Sentiment Analysis Project Assessment - Solutions
11:03
+ Topic Modeling
9 lectures 01:07:50
Introduction to Topic Modeling Section
00:39
Overview of Topic Modeling
02:03
Latent Dirichlet Allocation with Python - Part Two
16:33
Non-negative Matrix Factorization Overview
06:54
Non-negative Matrix Factorization with Python
11:42
Topic Modeling Project - Overview
03:42
Topic Modeling Project - Solutions
06:38
+ Deep Learning for NLP
15 lectures 02:38:11
Introduction to Deep Learning for NLP
00:53
The Basic Perceptron Model
05:12
Introduction to Neural Networks
06:35
Keras Basics - Part One
13:44
Keras Basics - Part Two
05:20
Recurrent Neural Network Overview
07:47
LSTMs, GRU, and Text Generation
10:10
Text Generation with LSTMs with Keras and Python - Part One
16:23
Text Generation with LSTMs with Keras and Python - Part Two
12:28
Text Generation with LSTMS with Keras - Part Three
13:56
Chat Bots Overview
07:17
Creating Chat Bots with Python - Part One
10:29
Creating Chat Bots with Python - Part Two
12:58
Creating Chat Bots with Python - Part Three
16:49
Creating Chat Bots with Python - Part Four
18:10
Requirements
  • Understand general Python
  • Have permissions to install python packages onto computer
  • Internet connection
Description

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


Who this course is for:
  • Python developers interested in learning how to use Natural Language Processing.