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Natural Language Processing (NLP)

Hands On Natural Language Processing (NLP) using Python

Hands On Natural Language Processing (NLP) using Python

Loại khoá học: Data Science

Learn Natural Language Processing ( NLP ) & Text Mining by creating text classifier, article summarizer, and many more.

50.000 VND

75 VND

Đầy Đủ Bài Giảng

Học Online Tiện Lợi

Kích Hoạt Nhanh 2-5 Phút

Thanh toán tự động

Được phép tải xuống

Mô tả

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.

Bạn sẽ học được gì

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

Yêu cầu

Basic Programming Experience in any language
Concept of Object Oriented Programming
Knowledge of Basic to Intermediate Mathematics
Knowledge of Matrix operations

Nội dung khoá học

11 sections

Introduction to the Course

3 lectures

What is NLP?

05:30

Getting the Course Resources

01:28

Getting the Course Resources - Text

00:16

Getting the required softwares

4 lectures

Installing Anaconda Python

03:04

Installing Anaconda Python - Text

00:24

A tour of Spyder IDE

04:23

How to take this course?

01:02

Python Crash Course

12 lectures

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

List Comprehension

13:12

Test Your Skills

4 questions

Regular Expressions

8 lectures

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

Introduction to Numpy

21:07

Introduction to Pandas

20:46

NLP Core

29 lectures

Installing NLTK in Python

04:16

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

Getting the data for Text Classification

05:29

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

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

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

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

Conclusion

1 lectures

Where you go from here?

00:31

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