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Learner Reviews & Feedback for Natural Language Processing with Probabilistic Models by deeplearning.ai

4.8
stars
100 ratings
13 reviews

About the Course

In Course 2 of the Natural Language Processing Specialization, offered by deeplearning.ai, 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 important 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. Please make sure that you’re comfortable programming in Python and have a basic knowledge of machine learning, matrix multiplications, and conditional probability. 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....

Top reviews

KK

Jul 02, 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.

AN

Jul 11, 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.

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1 - 15 of 15 Reviews for Natural Language Processing with Probabilistic Models

By Oleh S

Jul 13, 2020

This course is more mature than the first one. Materials are very interesting and provide nice intuition for the probabilistic models. One can study the basics of auto-correction, Markov and Hidden Markov Models as well as the N-gram models and very important approach - Word2Vec, which is the essential part of the modern deep learning algorithms. I really enjoyed this part.

However, there are some minor suggestions:

1. Lectures duration could be longer - it will help to provide more depth in materials in both math and code side. I know that this is a simple version of real academic course, but in order to increase the quality you should consider the increasing duration;

2. Programming assignments are not balanced and there are still some minor ambiguities. For instance, the first and HMM assignments are tough, whereas the last one is a piece of cake.

3. The course can be enhanced with the additional part dedicated to Probability Theory, maybe a few lectures more.

I recommend this course to everyone interested in NLP. Note, you should read and study the additional resources to reinforce your knowledge, here is just the starting point for a good researcher. Keep going, guys!

By Zhendong W

Jul 10, 2020

A great course indeed! However, it would be even nicer to have the lecture videos in a slower pace, maybe go through the examples in more detail. Sometimes it felt too quick to jump directly through the theory to examples.

By Christoph H

Jul 01, 2020

This course goes hand in hand with the Daniel Jurafsky's introduction to NLP (Speech and Language Processing) and provides the knowhow for hands on implementation of simple but powerful probabilistic methods.

By Saurabh K

Jul 14, 2020

I have a wonderful experience. Try not to look at the hints, resolve yourself, it is excellent course for getting the in depth knowledge of how the black boxes work. Happy learning.

By Kazuomi K

Jul 02, 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.

By Andrei N

Jul 11, 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.

By Bradley W

Jul 06, 2020

exceptional course, will be able to apply some of these learnings to my job :)

By Firefly S

Jul 06, 2020

This is really helpful. Thanks to all contributors!

By Shamanth N

Jul 02, 2020

As i had told before,this specialization is amazing

By Yuanwen W

Jun 27, 2020

Great quality! Very clear on word embeddings.

By Jakousi U C

Jul 05, 2020

This course was a little more challenging

By Zoltan S

Jun 27, 2020

An excellent course.

By Aung Z P

Jul 14, 2020

I love the way the instructor teach and the course design which is made to be simple but effective

By ES

Jul 08, 2020

Homework is too easy. The answers are pretty much given to us.

By Dan C

Jul 08, 2020

Lots of Quality Control issues. using paid customers as proofreaders is tacky.