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.
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: Data Cleansing
Status: Markov Model
IntermediateCourse30 hours
Featured reviews
NP
5.0Reviewed Jan 22, 2022
This class is one of the best on the subject. The prof is very knowledgeable and explains concepts very clearly. The code in the assignments and lectures is super clean and structured incredibly well.
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.
KK
5.0Reviewed Jul 1, 2020
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.
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!
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!
HS
5.0Reviewed Dec 2, 2020
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!!
RA
4.0Reviewed Jan 15, 2021
In the first and second week the exercices have some unecessery pranks in the data formatation just to make the exercice harded, but it take out the attention for what matter in the course that is NLP
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.
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.
AH
5.0Reviewed Oct 8, 2020
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.
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.