Natural Language Processing – Probability Models in Python
English | Tutorial | Size: 788.8 MB
In this 4 hour course, you will explore the world of Natural Language Processing (NLP) through practical Python programming examples. Learn how Markov models and probability methods can be applied to create classifiers, spin articles, and decrypt ciphers. Perfect for budding NLP developers and enthusiasts.
What I will be able to do after this course
Understand and apply Markov models in the context of probability-focused NLP.
Develop Python-based text classifiers using probability smoothing techniques.
Create and implement article spinners with the N-gram approach in Python.
Gain understanding of genetic algorithms for practical applications like cipher decryption.
Develop problem-solving skills through practical exercises in text analysis and security domains.
Course Instructor(s)
Your instructor, Lazy Programmer, brings years of experience in machine learning, data engineering, and course content creation. With a passion for simplifying complex topics, they have guided thousands of students worldwide through intuitive coding examples and engaging materials. Their teaching style ensures you can directly apply your new skills immediately.
DOWNLOAD:
RAPIDGATOR:
rapidgator.net/file/a81229f8447009618a2cf47280eeb8d3/Natural_Language_Processing_-_Probability_Models_in_Python.rar.html
rapidgator.net/file/48ebc39d9668e5646df62e01b5eff48e/Natural_Language_Processing_-_Probability_Models_in_Python.rar.html
NITROFLARE:
nitroflare.com/view/F6F298CB8CFEA0D/Natural_Language_Processing_-_Probability_Models_in_Python.rar
nitroflare.com/view/5B17522E4FDF769/Natural_Language_Processing_-_Probability_Models_in_Python.rar