Natural Language Processing - Probability Models in Python
About this course
This course now features Coursera Coach! A smarter way to learn with interactive, real-time conversations that help you test your knowledge, challenge assumptions, and deepen your understanding as you progress through the course. Dive into Natural Language Processing (NLP) using probability models in Python! This course covers essential topics like Markov models, text classification, article spinning, and cipher decryption. You will build practical skills by applying theoretical knowledge through coding exercises, enabling you to tackle real-world NLP problems with probability models. Begin by understanding the foundations of Markov models, including the Markov property and probability smoothing techniques. You will learn how to build and code text classifiers and language models, exploring the application of these models in text prediction. With hands-on coding exercises, you will master implementing these models in Python. Next, you will delve into article spinning using n-grams, enhancing your ability to generate diverse and meaningful content. Finally, you’ll explore the complexities of cipher decryption, applying probability models and genetic algorithms to crack encrypted messages. Throughout the course, you'll solidify your understanding by coding and testing various models. This course is perfect for learners interested in NLP, machine learning, and Python programming. No prior experience in probability modeling is required, though familiarity with Python basics is beneficial. Ideal for learners looking to strengthen their NLP and data science skills.
74/100
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- 38/45
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- 20/35
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What you'll learn
- understand the foundations of Markov models
- apply probability smoothing techniques
- build and code text classifiers
- implement language models for text prediction
- generate diverse content using article spinning and n-grams
- apply genetic algorithms to decrypt ciphers
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