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. 艁ukasz Kaiser is a Staff Research Scientist at Google Brain and the co-author of Tensorflow, the Tensor2Tensor and Trax libraries, and the Transformer paper.
Week Introduction
Skills You'll Learn
Autocorrect, N-gram Language Models, Parts-of-Speech Tagging, Statistical Programming, Machine Learning, Word2vec, Python Programming
Reviews
4.7 (1,752 ratings)
- 5 stars79.50%
- 4 stars14.89%
- 3 stars3.42%
- 2 stars0.85%
- 1 star1.31%
HS
Dec 3, 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!!
SR
Aug 5, 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.
From the lesson
Autocomplete and Language Models
Learn about how N-gram language models work by calculating sequence probabilities, then build your own autocomplete language model using a text corpus from Twitter!
Taught By
Younes Bensouda Mourri
Instructor
艁ukasz Kaiser
Instructor
Eddy Shyu
Instructor