Search icon CANCEL
Subscription
0
Cart icon
Your Cart (0 item)
Close icon
You have no products in your basket yet
Arrow left icon
Explore Products
Best Sellers
New Releases
Books
Videos
Audiobooks
Learning Hub
Conferences
Free Learning
Arrow right icon
Arrow up icon
GO TO TOP
40 Algorithms Every Programmer Should Know

You're reading from   40 Algorithms Every Programmer Should Know Hone your problem-solving skills by learning different algorithms and their implementation in Python

Arrow left icon
Product type Paperback
Published in Jun 2020
Publisher Packt
ISBN-13 9781789801217
Length 382 pages
Edition 1st Edition
Languages
Arrow right icon
Author (1):
Arrow left icon
Imran Ahmad Imran Ahmad
Author Profile Icon Imran Ahmad
Imran Ahmad
Arrow right icon
View More author details
Toc

Table of Contents (19) Chapters Close

Preface 1. Section 1: Fundamentals and Core Algorithms
2. Overview of Algorithms FREE CHAPTER 3. Data Structures Used in Algorithms 4. Sorting and Searching Algorithms 5. Designing Algorithms 6. Graph Algorithms 7. Section 2: Machine Learning Algorithms
8. Unsupervised Machine Learning Algorithms 9. Traditional Supervised Learning Algorithms 10. Neural Network Algorithms 11. Algorithms for Natural Language Processing 12. Recommendation Engines 13. Section 3: Advanced Topics
14. Data Algorithms 15. Cryptography 16. Large-Scale Algorithms 17. Practical Considerations 18. Other Books You May Enjoy

Algorithms for Natural Language Processing

This chapter introduces algorithms for natural language processing (NLP). This chapter proceeds from the theoretical to the practical in a progressive manner. It will first introduce the fundamentals of NLP, followed by the basic algorithms. Then, it will look at one of the most popular neural networks that is widely used to design and implement solutions for important use cases for textual data. We will then look at the limitations of NLP before finally learning how we can use NLP to train a machine learning model that can predict the polarity of movie reviews.

This chapter will consist of the following sections: 

  • Introducing NLP

  • Bag-of-words-based (BoW-based) NLP

  • Introduction to word embedding

  • Use of recurrent neural networks for NLP

  • Using NLP for sentiment analysis

  • Case study: movie review sentiment analysis

By the end...

lock icon The rest of the chapter is locked
Register for a free Packt account to unlock a world of extra content!
A free Packt account unlocks extra newsletters, articles, discounted offers, and much more. Start advancing your knowledge today.
Unlock this book and the full library FREE for 7 days
Get unlimited access to 7000+ expert-authored eBooks and videos courses covering every tech area you can think of
Renews at $19.99/month. Cancel anytime
Banner background image