Natural Language Processing with Probabilistic Models
In Course 2 of the Natural Language Processing Specialization, you will:a) Create a simple auto-correct algorithm using minimum edit distance and dynamic programming,
b) Apply the Viterbi Algorithm for part-of-speech (POS) tagging, which is vital for computational linguistics,
c) Write a better auto-complete algorithm using an N-gram language model, and
d) Write your own Word2Vec model that uses a neural network to compute word embeddings using a continuous bag-of-words model.
By the end of this Specialization, you will have designed NLP applications that perform question-answering and sentiment analysis, created tools to translate languages and summarize text, and even built a chatbot!
This Specialization is designed and taught by two experts in NLP, machine learning, and deep learning. Younes Bensouda Mourri is an Instructor of AI at Stanford University who also helped build the Deep Learning Specialization. Łukasz Kaiser is a Staff Research Scientist at Google Brain and the co-author of Tensorflow, the Tensor2Tensor and Trax libraries, and the Transformer paper.
Use dynamic programming, hidden Markov models, and word embeddings to implement autocorrect, autocomplete & identify part-of-speech tags for words.
Syllabus
Syllabus - What you will learn from this course
Week 1
Autocorrect
Week 2
Part of Speech Tagging and Hidden Markov Models
Week 3
Autocomplete and Language Models
Week 4
Word embeddings with neural networks
FAQ
When will I have access to the lectures and assignments?
Access to lectures and assignments depends on your type of enrollment. If you take a course in audit mode, you will be able to see most course materials for free. To access graded assignments and to earn a Certificate, you will need to purchase the Certificate experience, during or after your audit. If you don't see the audit option:
What will I get if I subscribe to this Specialization?
When you enroll in the course, you get access to all of the courses in the Specialization, and you earn a certificate when you complete the work. Your electronic Certificate will be added to your Accomplishments page - from there, you can print your Certificate or add it to your LinkedIn profile. If you only want to read and view the course content, you can audit the course for free.
Is financial aid available?
Yes. In select learning programs, you can apply for financial aid or a scholarship if you can’t afford the enrollment fee. If fin aid or scholarship is available for your learning program selection, you’ll find a link to apply on the description page.
Reviews
This course is very good introduction to NLP Probabilistic models such as Hidden Markov model, N-Gram Language model, and Word2Vec with Python programming assignments.
Thoroughly relished this course. Each and every concept is explained in depth as well as there is a companion notebook to explain as well as practically implement the concepts.
This is one of the best courses i have taken. I have learned a lot from this course. Assignments were great and challenging. Thank you deeplearning.ai team for this amazing course.
A neatly organized course introducing the students to basics of Processing text data, learning word embedding and most importantly on how to interpret the word embedding. Great Job!!
