Python for Natural Language Processing (NLP)

Python for Natural Language Processing (NLP), available at $64.99, has an average rating of 4.5, with 31 lectures, based on 43 reviews, and has 6139 subscribers.

You will learn about Text classification Sentiment Analysis Working with text data in Python Python Fundamentals Natural Language Processing (NLP) topics and applications This course is ideal for individuals who are People who is interested in Data Science and wants to learn Natural Language Processing (NLP) It is particularly useful for People who is interested in Data Science and wants to learn Natural Language Processing (NLP).

Enroll now: Python for Natural Language Processing (NLP)

Summary

Title: Python for Natural Language Processing (NLP)

Price: $64.99

Average Rating: 4.5

Number of Lectures: 31

Number of Published Lectures: 31

Number of Curriculum Items: 31

Number of Published Curriculum Objects: 31

Original Price: $19.99

Quality Status: approved

Status: Live

What You Will Learn

Text classification
Sentiment Analysis
Working with text data in Python
Python Fundamentals
Natural Language Processing (NLP) topics and applications

Who Should Attend

People who is interested in Data Science and wants to learn Natural Language Processing (NLP)

Target Audiences

People who is interested in Data Science and wants to learn Natural Language Processing (NLP)

Welcome to the landing page of Python for Natural Language Processing (NLP) course. This course is built for students who want to learn NLP concepts in Python. Course starts with the repeat of the Python Fundamentals. After it text methods and pandas library is covered in the course. Text methods will be helpful when we are going to be building Natural Language Processing projects. We will use pandas library for reading and analyzing our data sets. After it we will cover some fatures of spaCy library like part of speech tagging, tokenization and named entity recognition. spaCy with NLTK are the both most popular Python libraries for Natural Language Processing. After covering that concepts we will move into evaluation of model performances section and there we will be learning how the NLP models will be evaluated. After that task we will see Sentiment Analysis and Text Classification and we will make examples of them. At the final lectures of the course we will build a Natural Language Processing project from stratch with what we learned through the course and we will finish. At the whole course process and after it, students can reach to me about the course concepts via Q&A section of the course or direct messages on Udemy. Thanks for visiting course page and reading course description.

Course Curriculum

Chapter 1: Before starting to the course

Lecture 1: First Lecture

Chapter 2: Pandas

Lecture 1: Pandas part 1

Lecture 2: Pandas part 2

Chapter 3: Text Methods

Lecture 1: Text methods part 1

Lecture 2: Text methods part 2

Lecture 3: Text methods part 3

Chapter 4: spaCy Library and NLP Concepts

Lecture 1: Introduction to spaCy & Tokenization

Lecture 2: Part of Speech Tagging & Named Entity Recognition

Chapter 5: Evaluation of Model Performances

Lecture 1: Train-Test Split

Lecture 2: Confusion Matrix

Chapter 6: Data Analysis of Course Data Set

Lecture 1: You can download the course data set

Lecture 2: Data Analysis part 1

Lecture 3: Data Analysis part 2

Chapter 7: Sentiment Analysis

Lecture 1: Sentiment Analysis part 1

Lecture 2: Sentiment Analysis part 2

Chapter 8: Text Classification

Lecture 1: Text Classification part 1

Lecture 2: Text Classification part 2

Chapter 9: NLP Project

Lecture 1: You can download the data set

Lecture 2: Exploring data

Lecture 3: Sentiment analysis

Lecture 4: Text Classification

Chapter 10: NLP Project 2

Lecture 1: Data Set

Lecture 2: Data Analysis

Lecture 3: Sentiment Analysis

Lecture 4: Text Classification

Chapter 11: NLP Project 3

Lecture 1: Data Set

Lecture 2: Data Analysis

Lecture 3: Data Analysis II

Lecture 4: Sentiment Analysis

Lecture 5: Text Classification

Chapter 12: Bonus Section

Lecture 1: bonus lecture

Instructors

Onur Baltacı
Data Scientist

Rating Distribution

1 stars: 1 votes
2 stars: 0 votes
3 stars: 8 votes
4 stars: 7 votes
5 stars: 27 votes

Frequently Asked Questions

How long do I have access to the course materials?

You can view and review the lecture materials indefinitely, like an on-demand channel.

Can I take my courses with me wherever I go?

Definitely! If you have an internet connection, courses on Udemy are available on any device at any time. If you don’t have an internet connection, some instructors also let their students download course lectures. That’s up to the instructor though, so make sure you get on their good side!

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