Hands On Natural Language Processing (NLP) using Python

Hands On Natural Language Processing (NLP) using Python, available at $79.99, has an average rating of 4.63, with 93 lectures, 2 quizzes, based on 1641 reviews, and has 10229 subscribers.

You will learn about 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 This course is ideal for individuals who are Anyone willing to start a career in data science and natural language processing or Anyone willing to learn the concepts of natural language processing by implementing them or Anyone willing to learn Sentiment Analysis It is particularly useful for Anyone willing to start a career in data science and natural language processing or Anyone willing to learn the concepts of natural language processing by implementing them or Anyone willing to learn Sentiment Analysis.

Enroll now: Hands On Natural Language Processing (NLP) using Python

Summary

Title: Hands On Natural Language Processing (NLP) using Python

Price: $79.99

Average Rating: 4.63

Number of Lectures: 93

Number of Quizzes: 2

Number of Published Lectures: 93

Number of Published Quizzes: 2

Number of Curriculum Items: 95

Number of Published Curriculum Objects: 95

Original Price: $89.99

Quality Status: approved

Status: Live

What You Will 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

Who Should Attend

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

Target Audiences

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

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.

Course Curriculum

Chapter 1: Introduction to the Course

Lecture 1: What is NLP?

Lecture 2: Getting the Course Resources

Lecture 3: Getting the Course Resources – Text

Chapter 2: Getting the required softwares

Lecture 1: Installing Anaconda Python

Lecture 2: Installing Anaconda Python – Text

Lecture 3: A tour of Spyder IDE

Lecture 4: How to take this course?

Chapter 3: Python Crash Course

Lecture 1: Variables and Operations in Python

Lecture 2: Conditional Statements

Lecture 3: Introduction to Loops

Lecture 4: Loop Control Statements

Lecture 5: Python Data Structures – Lists

Lecture 6: Python Data Structures – Tuples

Lecture 7: Python Data Structures – Dictionaries

Lecture 8: Console and File I/O in Python

Lecture 9: Introduction to Functions

Lecture 10: Introduction to Classes and Objects

Lecture 11: List Comprehension

Chapter 4: Regular Expressions

Lecture 1: Introduction to Regular Expressions

Lecture 2: Finding Patterns in Text Part 1

Lecture 3: Finding Patterns in Text Part 2

Lecture 4: Substituting Patterns in Text

Lecture 5: Shorthand Character Classes

Lecture 6: Character Ranges – Text

Lecture 7: Preprocessing using Regex

Chapter 5: Numpy and Pandas

Lecture 1: Introduction to Numpy

Lecture 2: Introduction to Pandas

Chapter 6: NLP Core

Lecture 1: Installing NLTK in Python

Lecture 2: Tokenizing Words and Sentences

Lecture 3: How tokenization works? – Text

Lecture 4: Introduction to Stemming and Lemmatization

Lecture 5: Stemming using NLTK

Lecture 6: Lemmatization using NLTK

Lecture 7: Stop word removal using NLTK

Lecture 8: Parts Of Speech Tagging

Lecture 9: POS Tag Meanings

Lecture 10: Named Entity Recognition

Lecture 11: Text Modelling using Bag of Words Model

Lecture 12: Building the BOW Model Part 1

Lecture 13: Building the BOW Model Part 2

Lecture 14: Building the BOW Model Part 3

Lecture 15: Building the BOW Model Part 4

Lecture 16: Text Modelling using TF-IDF Model

Lecture 17: Building the TF-IDF Model Part 1

Lecture 18: Building the TF-IDF Model Part 2

Lecture 19: Building the TF-IDF Model Part 3

Lecture 20: Building the TF-IDF Model Part 4

Lecture 21: Understanding the N-Gram Model

Lecture 22: Building Character N-Gram Model

Lecture 23: Building Word N-Gram Model

Lecture 24: Understanding Latent Semantic Analysis

Lecture 25: LSA in Python Part 1

Lecture 26: LSA in Python Part 2

Lecture 27: Word Synonyms and Antonyms using NLTK

Lecture 28: Word Negation Tracking in Python Part 1

Lecture 29: Word Negation Tracking in Python Part 2

Chapter 7: Project 1 – Text Classification

Lecture 1: Getting the data for Text Classification

Lecture 2: Getting the data for Text Classification – Text

Lecture 3: Importing the dataset

Lecture 4: Persisting the dataset

Lecture 5: Preprocessing the data

Lecture 6: Transforming data into BOW Model

Lecture 7: Transform BOW model into TF-IDF Model

Lecture 8: Creating training and test set

Lecture 9: Understanding Logistic Regression

Lecture 10: Training our classifier

Lecture 11: Testing Model performance

Lecture 12: Saving our Model

Lecture 13: Importing and using our Model

Chapter 8: Project 2 – Twitter Sentiment Analysis

Lecture 1: Setting up Twitter Application

Lecture 2: Initializing Tokens

Lecture 3: Client Authentication

Lecture 4: Fetching real time tweets

Lecture 5: Loading TF-IDF Model and Classifier

Lecture 6: Preprocessing the tweets

Lecture 7: Predicting sentiments of tweets

Lecture 8: Plotting the results

Chapter 9: Project 3 – Text Summarization

Lecture 1: Understanding Text Summarization

Lecture 2: Fetching article data from the web

Lecture 3: Parsing the data using Beautiful Soup

Lecture 4: Preprocessing the data

Lecture 5: Tokenizing Article into sentences

Lecture 6: Building the histogram

Lecture 7: Calculating the sentence scores

Lecture 8: Getting the summary

Chapter 10: Word2Vec Analysis

Lecture 1: Understanding Word Vectors

Lecture 2: Importing the data

Lecture 3: Preparing the data

Instructors

Next Edge Coding
Full Stack Developer & Data Enthusiast

Rating Distribution

1 stars: 25 votes

2 stars: 28 votes

3 stars: 192 votes

4 stars: 622 votes

5 stars: 774 votes

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