Explore Probability Models and Markov Chains for NLP with Python Programming

€90.99

By Lazy Programmer

Time 4hrs 22mins

Published in Jun 2024

Product Type Video

Edition 1st Edition

ISBN 9781836201854

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Key Benefits

Markov models are explored to provide a solid foundation for various NLP applications

Article spinning with N-gram techniques is covered to enhance automated content generation

Genetic algorithms are introduced for practical cipher decryption in cybersecurity contexts

What You Will Learn

Build and implement Markov models for NLP tasks

Apply probability smoothing and log-probabilities to optimize models

Develop a text classifier in Python using Markov models

Create an article spinner using the N-gram approach

Decrypt ciphers using genetic algorithms in Python

Analyze real-world NLP use cases like cybersecurity and content generation

Who Is This Book For?

This course is designed for intermediate Python developers, data scientists, and machine learning enthusiasts with a background in Python programming. If you're keen on mastering NLP techniques and probability models, this course will provide the tools you need. It is perfect for professionals seeking to expand their skill set in NLP, text analysis, or security-related applications. Familiarity with Python basics and probability concepts is recommended but not required.

Book Description

This course offers an in-depth exploration of Natural Language Processing (NLP) using Python, starting with Markov models. You’ll learn the Markov property, then build a text classifier, applying probability smoothing and log-probabilities to improve your models. These foundational concepts will help you gain confidence in using Python for NLP tasks. The course then shifts to article spinning, where you’ll apply the N-gram approach to generate content. You’ll develop an article spinner in Python, while also analyzing the challenges and ethical considerations of automated content creation. This section emphasizes practical coding and real-world application. The final section tackles cipher decryption, where you’ll use genetic algorithms to crack codes. By working through real-world case studies, including the acoustic keylogger example, you’ll see how NLP and machine learning techniques are applied in fields like cybersecurity. By the end of the course, you’ll have the skills to tackle complex NLP tasks with Python in various domains.

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Natural Language Processing - Probability Models in Python

Explore Probability Models and Markov Chains for NLP with Python Programming

What do you get with a video?

Key Benefits

What You Will Learn

Who Is This Book For?

Book Description

Frequently bought together

Recommendations for you

About the author

FAQs

Natural Language Processing - Probability Models in Python

Explore Probability Models and Markov Chains for NLP with Python Programming

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What do you get with a video?

Key Benefits

What You Will Learn

Who Is This Book For?

Book Description

Frequently bought together

Recommendations for you

About the author

FAQs

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