Natural Language Processing with Probabilistic Models
Master probabilistic approaches to NLP including autocorrect, POS tagging, language models, and word embeddings.
Master probabilistic approaches to NLP including autocorrect, POS tagging, language models, and word embeddings.
This comprehensive course explores probabilistic methods in Natural Language Processing. Students learn to implement autocorrect using minimum edit distance, apply Hidden Markov Models for part-of-speech tagging, create N-gram language models for autocomplete, and develop word embeddings using neural networks. Through hands-on programming assignments, learners build practical NLP tools while understanding the underlying mathematical concepts.
4.7
(1,704 ratings)
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English
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What you'll learn
Build an autocorrect system using minimum edit distance
Implement part-of-speech tagging using Hidden Markov Models
Create N-gram language models for text prediction
Develop word embeddings using neural networks
Evaluate language models using intrinsic and extrinsic methods
Skills you'll gain
This course includes:
3.32 Hours PreRecorded video
8 assignments
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There are 4 modules in this course
This course provides a thorough exploration of probabilistic approaches in Natural Language Processing. The curriculum is structured around four key modules: autocorrect systems using dynamic programming, part-of-speech tagging with Hidden Markov Models, N-gram language models for autocomplete, and word embeddings using neural networks. Each module combines theoretical foundations with practical implementation, featuring extensive programming assignments and real-world applications.
Autocorrect
Module 1 · 6 Hours to complete
Part of Speech Tagging and Hidden Markov Models
Module 2 · 5 Hours to complete
Autocomplete and Language Models
Module 3 · 8 Hours to complete
Word embeddings with neural networks
Module 4 · 9 Hours to complete
Fee Structure
Instructors
Stanford AI Educator Pioneers Global Learning Through Course Innovation and EdTech Leadership
Younes Bensouda Mourri is a distinguished AI educator and entrepreneur who has significantly impacted global tech education. Born and raised in Morocco, he earned his B.S. in Applied Mathematics and Computer Science and M.S. in Statistics from Stanford University, where he now teaches Artificial Intelligence both on campus and online. As the founder of LiveTech.AI, he develops AI tools to transform academic institutions, while his courses have reached over 1.3 million learners worldwide, with 23% securing AI-related jobs after completion. His contributions include co-creating Stanford's Applied Machine Learning, Deep Learning, and Teaching AI courses, as well as developing the highly successful Natural Language Processing Specialization for DeepLearning.AI. Starting as a teaching assistant in Andrew Ng's Machine Learning course, he rose to become an Adjunct Lecturer at Stanford by age 22, demonstrating his commitment to democratizing AI education. Through his work with major companies like ASML, CISCO, and Boston Consulting Group, he continues to advance AI education while focusing on developing innovative NLP tools for personalized feedback and chain-of-thought reasoning
Leading Innovator in AI and Deep Learning Education
Łukasz Kaiser is a prominent instructor at DeepLearning.AI, recognized for his significant contributions to the field of artificial intelligence. As a co-author of TensorFlow, the Tensor2Tensor and Trax libraries, and the influential Transformer paper, he has played a pivotal role in shaping modern AI methodologies. Currently serving as a Staff Research Scientist at Google Brain, Łukasz's research has profoundly impacted the AI community, particularly in the realm of natural language processing (NLP). His courses on Coursera, including "Natural Language Processing with Attention Models" and "Natural Language Processing with Sequence Models," provide learners with essential skills to navigate and implement advanced AI techniques.With a strong academic background and extensive experience in machine learning, Łukasz is dedicated to making complex concepts accessible to students worldwide. He teaches multiple courses in various languages, including French, Russian, Arabic, and Korean, demonstrating his commitment to global education. His work not only enhances the understanding of deep learning but also empowers aspiring data scientists and AI practitioners to develop innovative solutions in their respective fields. Through his engaging teaching style and expert knowledge, Łukasz Kaiser continues to inspire the next generation of AI professionals.
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4.7 course rating
1,704 ratings
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