NLP and Text mining with python(for absolute beginners only)

NLP and Text mining with python(for absolute beginners only), available at $39.99, has an average rating of 3.65, with 33 lectures, based on 416 reviews, and has 9955 subscribers.

You will learn about After the completion of this course, you will have good understanding of NLP. You will approach algorthms to solve real world NLP problems. You will be able to implement sentiment analysis You will be able to perform document classification This course is ideal for individuals who are Data Scientists who are curious about NLP. or Data Analysts working on text data and want to get some insight using NLP. or Students who want to learn NLP. It is particularly useful for Data Scientists who are curious about NLP. or Data Analysts working on text data and want to get some insight using NLP. or Students who want to learn NLP.

Enroll now: NLP and Text mining with python(for absolute beginners only)

Summary

Title: NLP and Text mining with python(for absolute beginners only)

Price: $39.99

Average Rating: 3.65

Number of Lectures: 33

Number of Published Lectures: 33

Number of Curriculum Items: 33

Number of Published Curriculum Objects: 33

Original Price: $19.99

Quality Status: approved

Status: Live

What You Will Learn

After the completion of this course, you will have good understanding of NLP.
You will approach algorthms to solve real world NLP problems.
You will be able to implement sentiment analysis
You will be able to perform document classification

Who Should Attend

Data Scientists who are curious about NLP.
Data Analysts working on text data and want to get some insight using NLP.
Students who want to learn NLP.

Target Audiences

Data Scientists who are curious about NLP.
Data Analysts working on text data and want to get some insight using NLP.
Students who want to learn NLP.

Want to know how NLP algorithms work and how people apply it to solve data science problems? You are looking at right course!

This course has been created, designed and assembled by professional Data Scientist who have worked in this field for nearly a decade. We can help you to understand the NLP while keeping you grounded to the implementation on real and data science problems.

We are sure that you will have fun while learning from our tried and tested structure of course to keep you interested in what coming next.

Here is how the course is going to work:

Session 1: Get introduced to NLP and text mining basics, NLTK package and learn how to prepare unstructured data for further processing.
Session 2: Lets you understand sentimental analysis using a case study and a practice session.
Session 3: Teaches you document categorization using various machine learning algorithms.

Features: �

Fully packed with LAB Sessions. One to learn from and one to do it by yourself. �
Course includes Python code, Datasets, ipython notebook and other supporting material at the beginning of each section for you to download and use on your own.

Course Curriculum

Chapter 1: Introduction to NLP and Text Mining

Lecture 1: Introduction to Text Mining

Lecture 2: Need for Preparation Before Text Mining

Lecture 3: Getting Ready for Data Preparation

Lecture 4: Reading Text Data as Corpus

Lecture 5: Text Data Cleaning Stages

Lecture 6: Tokenizing

Lecture 7: Stop Words

Lecture 8: Stemming and Lemmatizing

Lecture 9: Final Cleaning using Regular Expressions

Lecture 10: LAB_Data Cleaning Case Study on News Data

Lecture 11: Document Term Matrix

Lecture 12: LAB_ Document Term Matrix

Lecture 13: NLP and Text Mining Basics Conclusion

Chapter 2: Sentiment Analysis

Lecture 1: Introduction to Sentiment Analysis

Lecture 2: Sentiment Analysis Algorithms

Lecture 3: LAB Case Study_ Importing Movies Review Data

Lecture 4: LAB Case Study Creating DTM

Lecture 5: LAB Case Study Refining DTM

Lecture 6: Understanding Bayes Theorem P1

Lecture 7: Understanding Bayes Theorem P2

Lecture 8: Bayes Theorem to Naive Bayes Model

Lecture 9: LAB Building and Validating Sentiment Analysis Model

Lecture 10: Sentiment Analysis Conclusion

Chapter 3: Document Categorisation

Lecture 1: Introduction to Document Categorisation

Lecture 2: Document categorisation using Naive Bays model

Lecture 3: LAB Importing data and Preprocessing

Lecture 4: LAB Preparing NLTK Friendly Feature Set

Lecture 5: LAB Model Building and Exploring Results

Lecture 6: LAB Multiple Document Categorisation

Lecture 7: SVM Model for Text Classification

Lecture 8: Understanding SVM Hyper Parameters to improve model

Lecture 9: LAB Tuning SVM Hyper Parameters to Improve Model

Lecture 10: Document Categorisation Conclusion

Instructors

Venkata Reddy AI Classes
Data Science starts here!

Rating Distribution

1 stars: 10 votes
2 stars: 16 votes
3 stars: 82 votes
4 stars: 168 votes
5 stars: 140 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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