RV
4.0Reviewed Aug 18, 2020
The course is exceptional in its own way by bringing people to the understanding of probabilistic models. Crisp & Clear. But one need to explore & practise more to gain expertise.
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. 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.
Status: Deep Learning
Status: Markov Model
IntermediateCourse30 hours
Featured reviews
BN
5.0Reviewed Sep 10, 2020
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.
AL
5.0Reviewed Feb 2, 2021
Course well structured. SBOW very well explained and registered firmly. Word embeddings explained very well. Overall very happy from the learning’s
SR
5.0Reviewed Aug 4, 2021
Another great course introducing the probabilistic modelling concepts and slowly getting to the direction of computing neural networks. One must learn in detail how embedding works.
AH
5.0Reviewed Sep 28, 2020
Very good course! helped me clearly learn about Autocorrect, edit distance, Markov chains, n grams, perplexity, backoff, interpolation, word embeddings, CBOW. This was very helpful!
BS
5.0Reviewed Aug 29, 2020
Totally enjoyed it. I took a Deep Learning course a couple of years ago and in some respect, it was a great refreshment form two years ago. Thank you!
AB
4.0Reviewed Jun 17, 2022
Week 4 Lab Assignment could be made a little bit tougher. The backpropagation derivation of W1, W2, b1 and b2 could have an optional reading for the interested reader. Otherwise, amazing course!
KM
5.0Reviewed Aug 9, 2020
This course is great. Actually the NLP specialization so far has been really good. The lectures are short and interesting and you get a good grasp on the concepts.
BN
5.0Reviewed Feb 12, 2021
Nicely broken into digestible chunks. Labs well done, not too easy, and too too frustrating. Material presented clearly and in (again) nice small steps.
AP
5.0Reviewed Dec 27, 2020
A great course in the very spirit of the original Andrew Ng's ML course with lots of details and explanations of fundamental approaches and techniques.
PP
5.0Reviewed May 29, 2021
I'm really thankful to the professors for sharing there knowledge and experience and creating this excellent course. I have learnt a a lot. Thank You !!!
OO
4.0Reviewed Oct 6, 2020
Good course, but the lecture notes in week 2 can be much more improved. Understanding Viterbi algorithm without visuals and animations was very difficult. Apart from that, great course!