Hands On Natural Language Processing (NLP) using Python
4.3 (1,040 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.
5,818 students enrolled

Hands On Natural Language Processing (NLP) using Python

Learn Natural Language Processing ( NLP ) & Text Mining by creating text classifier, article summarizer, and many more.
Bestseller
4.3 (1,040 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.
5,818 students enrolled
Created by Next Edge Coding
Last updated 9/2019
English
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Current price: $59.99 Original price: $99.99 Discount: 40% off
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This course includes
  • 10.5 hours on-demand video
  • 8 articles
  • Full lifetime access
  • Access on mobile and TV
  • Certificate of Completion
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What you'll learn
  • Understand the various concepts of natural language processing along with their implementation
  • Build natural language processing based applications
  • Learn about the different modules available in Python for NLP
  • Create personal spam filter or sentiment predictor
  • Create personal text summarizer
Course content
Expand all 93 lectures 10:33:22
+ Introduction to the Course
3 lectures 07:14
Getting the Course Resources
01:28
Getting the Course Resources - Text
00:16
+ Getting the required softwares
4 lectures 08:53
Installing Anaconda Python - Text
00:24
A tour of Spyder IDE
04:23
How to take this course?
01:02
+ Python Crash Course
11 lectures 01:37:10
Variables and Operations in Python
07:49
Conditional Statements
06:37
Introduction to Loops
07:43
Loop Control Statements
07:38
Python Data Structures - Lists
13:24
Python Data Structures - Tuples
05:55
Python Data Structures - Dictionaries
11:51
Console and File I/O in Python
08:26
Introduction to Functions
06:59
Introduction to Classes and Objects
07:36
Test Your Skills
4 questions
+ Regular Expressions
7 lectures 50:28
Introduction to Regular Expressions
04:36
Finding Patterns in Text Part 1
08:54
Finding Patterns in Text Part 2
08:16
Substituting Patterns in Text
06:04
Shorthand Character Classes
14:41
Character Ranges - Text
00:45
Preprocessing using Regex
07:12
Test Your Skills
2 questions
+ Numpy and Pandas
2 lectures 41:53
Introduction to Numpy
21:07
Introduction to Pandas
20:46
+ NLP Core
29 lectures 04:03:05
Tokenizing Words and Sentences
04:18
How tokenization works? - Text
00:49
Introduction to Stemming and Lemmatization
07:37
Stemming using NLTK
07:23
Lemmatization using NLTK
04:12
Stop word removal using NLTK
07:46
Parts Of Speech Tagging
06:13
POS Tag Meanings
00:31
Named Entity Recognition
05:25
Text Modelling using Bag of Words Model
10:47
Building the BOW Model Part 1
04:47
Building the BOW Model Part 2
04:50
Building the BOW Model Part 3
04:30
Building the BOW Model Part 4
06:55
Text Modelling using TF-IDF Model
16:28
Building the TF-IDF Model Part 1
07:16
Building the TF-IDF Model Part 2
08:26
Building the TF-IDF Model Part 3
07:22
Building the TF-IDF Model Part 4
04:06
Understanding the N-Gram Model
19:40
Building Character N-Gram Model
16:30
Building Word N-Gram Model
11:55
Understanding Latent Semantic Analysis
14:08
LSA in Python Part 1
20:40
LSA in Python Part 2
12:32
Word Synonyms and Antonyms using NLTK
09:02
Word Negation Tracking in Python Part 1
09:06
Word Negation Tracking in Python Part 2
05:34
+ Project 1 - Text Classification
13 lectures 01:09:09
Getting the data for Text Classification - Text
00:25
Importing the dataset
05:40
Persisting the dataset
05:27
Preprocessing the data
04:37
Transforming data into BOW Model
07:42
Transform BOW model into TF-IDF Model
03:00
Creating training and test set
04:29
Understanding Logistic Regression
14:56
Training our classifier
01:59
Testing Model performance
05:29
Saving our Model
05:57
Importing and using our Model
03:59
+ Project 2 - Twitter Sentiment Analysis
8 lectures 35:41
Setting up Twitter Application
03:38
Initializing Tokens
04:26
Client Authentication
03:33
Fetching real time tweets
05:30
Loading TF-IDF Model and Classifier
02:18
Preprocessing the tweets
07:36
Predicting sentiments of tweets
02:06
Plotting the results
06:34
+ Project 3 - Text Summarization
8 lectures 40:03
Understanding Text Summarization
06:38
Fetching article data from the web
04:39
Parsing the data using Beautiful Soup
07:11
Preprocessing the data
03:22
Tokenizing Article into sentences
03:15
Building the histogram
04:02
Calculating the sentence scores
06:18
Getting the summary
04:38
+ Word2Vec Analysis
7 lectures 39:13
Understanding Word Vectors
11:28
Importing the data
06:06
Preparing the data
03:07
Training the Word2Vec Model
02:34
Testing Model Performance
03:39
Improving the Model
06:34
Exploring Pre-trained Models
05:45
Requirements
  • Basic Programming Experience in any language
  • Concept of Object Oriented Programming
  • Knowledge of Basic to Intermediate Mathematics
  • Knowledge of Matrix operations
Description

In this course you will learn the various concepts of natural language processing by implementing them hands on in python programming language. This course is completely project based and from the start of the course the main objective would be to learn all the concepts required to finish the different projects. You will be building a text classifier which you will use to predict sentiments of tweets in real time and you will also be building an article summarizer which will fetch articles from websites and find the summary. Apart from these you will also be doing a lot of mini projects through out the course. So, at the end of the course you will have a deep understanding of NLP and how it is applied in real world.

Who this course is for:
  • Anyone willing to start a career in data science and natural language processing
  • Anyone willing to learn the concepts of natural language processing by implementing them
  • Anyone willing to learn Sentiment Analysis