Natural Language Processing - Probability Models in Python: Explore Probability Models and Markov Chains for NLP with Python Programming

Lazy Programmer

NZ$176.99

Video Jun 2024 4hrs 22mins 1st Edition

Lazy Programmer

NZ$176.99

Video Jun 2024 4hrs 22mins 1st Edition

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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

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.

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.