Natural Language Processing Specialization
Instructors: Younes Bensouda Mourri, Łukasz Kaiser
Also available on Coursera
Natural Language Processing Specialization
Intermediate
4 Courses
180 Video Lessons
164 Reading Lessons
40 Practices
14 Graded Assignments
Instructors: Younes Bensouda Mourri, Łukasz Kaiser
DeepLearning.AI
What you'll learn
Use logistic regression, naïve Bayes, and word vectors to implement sentiment analysis, complete analogies, translate words, and use locality-sensitive hashing to approximate nearest neighbors.
Use dynamic programming, hidden Markov models, and word embeddings to autocorrect misspelled words, autocomplete partial sentences, and identify part-of-speech tags for words.
Use dense and recurrent neural networks, LSTMs, GRUs, and Siamese networks in TensorFlow to perform advanced sentiment analysis, text generation, named entity recognition, and to identify duplicate questions.
Use encoder-decoder, causal, and self-attention to perform advanced machine translation of complete sentences, text summarization, and question-answering. Learn models like T5, BERT, and more with Hugging Face Transformers!
Skills you will gain
Natural language processing is a subfield of linguistics, computer science, and artificial intelligence that uses algorithms to interpret and manipulate human language.
In the Natural Language Processing (NLP) Specialization, you will learn how to design NLP applications that perform question-answering and sentiment analysis, create tools to translate languages, and summarize text. These and other NLP applications will be at the forefront of the coming transformation to an AI-powered future.
NLP is one of the most broadly applied areas of machine learning and is critical in effectively analyzing massive quantities of unstructured, text-heavy data. As AI continues to expand, so will the demand for professionals skilled at building models that analyze speech and language, uncover contextual patterns, and produce insights from text and audio.
Instructors
Course Slides
You can download the annotated version of the course slides below.
Frequently Asked Questions
Course Outline
49 Video Lessons • 47 Reading Lessons • 9 Practices • 4 Graded Assignments
Welcome to the NLP Specialization
Video • 4 mins
Welcome to Course 1
Video • 1 min
Acknowledgement - Ken Church
Reading • 10 mins
Week Introduction
Video • 1 min
Supervised ML & Sentiment Analysis
Video • 2 mins
Supervised ML & Sentiment Analysis
Reading • 2 mins
Vocabulary & Feature Extraction
Video • 2 mins
Vocabulary & Feature Extraction
Reading • 2 mins
Negative and Positive Frequencies
Video • 2 mins
Feature Extraction with Frequencies
Video • 2 mins
Feature Extraction with Frequencies
Reading • 10 mins
Preprocessing
Video • 3 mins
Preprocessing
Reading • 10 mins
Natural Language preprocessing
Code Example • 1 hour
Putting it All Together
Video • 2 mins
Putting it all together
Reading • 10 mins
Visualizing word frequencies
Code Example • 1 hour
Logistic Regression Overview
Video • 3 mins
Logistic Regression Overview
Reading • 10 mins
Logistic Regression: Training
Video • 1 min
Logistic Regression: Training
Reading • 10 mins
Visualizing tweets and Logistic Regression models
Code Example • 1 hour
Logistic Regression: Testing
Video • 4 mins
Logistic Regression: Testing
Reading • 10 mins
Logistic Regression: Cost Function
Video • 5 mins
Optional Logistic Regression: Cost Function
Reading • 10 mins
Week Conclusion
Video • 1 min
Optional Logistic Regression: Gradient
Reading • 10 mins
Join the DeepLearning.AI Forum to ask questions, get support, or share amazing ideas!
Reading • 2 mins
Lecture Notes W1
Reading • 1 min
Logistic Regression
Graded・Quiz • 10 mins
(Optional) Downloading your Notebook, Downloading your Workspace and Refreshing your Workspace
Reading • 5 mins
Logistic Regression
Graded・Code Assignment • 3 hours
Andrew Ng with Chris Manning
Video • 46 mins
Natural Language Processing Specialization
Intermediate
4 Courses
180 Video Lessons
164 Reading Lessons
40 Practices
14 Graded Assignments
Instructors: Younes Bensouda Mourri, Łukasz Kaiser
DeepLearning.AI
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