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Younes Bensouda Mourri,
Łukasz Kaiser,
and
Eddy Shyu
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.
Read more
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.
Read more
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, 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.
Register for this course and see more details by visiting:
OpenCourser.com/course/gqnh0t/natural
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What's inside
Syllabus
Autocorrect
Learn about autocorrect, minimum edit distance, and dynamic programming, then build your own spellchecker to correct misspelled words!
Read more
Syllabus
Autocorrect
Learn about autocorrect, minimum edit distance, and dynamic programming, then build your own spellchecker to correct misspelled words!
Read more
Traffic lights
Traffic lights
Read about what's good
what should give you pause and
possible dealbreakers
Focuses on core aspects of NLP, including POS tagging, language models, and word embeddings, which are essential for NLP practitioners
Taught by industry experts, Eddy Shyu and Łukasz Kaiser, who are renowned for their contributions to NLP and deep learning
Provides hands-on experience in building NLP applications using tools and algorithms like Viterbi Algorithm and N-gram language models
Course progression builds upon foundational concepts, making it suitable for learners with some background in NLP
Covers advanced topics such as Word2Vec and neural network-based word embeddings, equipping learners with in-demand skills
Requires familiarity with Python and basic machine learning concepts as prerequisites
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Reviews summary
Foundational probabilistic and neural nlp
According to learners, this course offers a robust and foundational understanding of Natural Language Processing, effectively bridging probabilistic models with an essential introduction to neural network-based word embeddings. Students consistently highlight the exceptional clarity of explanations for complex mathematical concepts and the insightful instruction provided by experts in the field. The inclusion of practical coding assignments and labs is frequently lauded for enabling students to apply theoretical knowledge, though some note the significant mathematical rigor may require a strong prior background. This makes it an ideal stepping stone for developing advanced NLP applications.
Provides strong basics, but further study is needed for advanced topics.
"It's a great course for understanding the fundamentals before diving into more modern deep learning NLP."
"This course lays a solid groundwork, but for cutting-edge topics like Transformers, you'll need more."
"I appreciated the strong focus on classic models, which are still very relevant and often overlooked now."
Taught by highly knowledgeable and experienced leaders in NLP.
"The instructors' deep knowledge and experience truly shine through, making the learning experience top-notch."
"Knowing it's taught by Google Brain and Stanford experts gives me immense confidence in the material."
"Their explanations were very clear, and they had a way of simplifying really complex ideas for us."
Practical assignments reinforce learning and build real-world skills.
"The programming assignments were challenging but incredibly rewarding, especially building my own spellchecker."
"I loved the hands-on aspect; coding the N-gram model from scratch truly solidified my understanding."
"The labs provided the perfect opportunity to apply what I learned in the lectures, making the concepts stick."
Offers exceptional clarity in explaining complex NLP theories.
"The way they broke down complex algorithms like Viterbi and HMMs made them surprisingly understandable."
"I finally grasped the math behind N-grams and Word2Vec; the lectures were incredibly precise."
"This course helped me connect the theoretical concepts to the practical applications seamlessly."
Requires a solid background in math, statistics, and programming.
"Be warned, the math can be intense. A good grasp of linear algebra and probability is a must."
"I struggled a bit with the theoretical derivations; prior exposure to discrete math would be helpful."
"This course is not for complete beginners in ML; you need some foundational programming and math skills."
Activities
Be better prepared
before
your course. Deepen your understanding
during
and
after
it. Supplement your coursework and achieve mastery of the topics covered
in Natural Language Processing with Probabilistic Models with these
activities:
Review Probability and Statistics
Show steps
Review probability and statistics to strengthen your understanding of NLP concepts that rely on probabilistic models and statistical techniques.
Browse courses on
Probability
Show steps
-
Review basic probability concepts such as conditional probability and Bayes' theorem.
-
Review basic statistics concepts such as mean, variance, and standard deviation.
-
Practice solving probability and statistics problems.
-
Complete online quizzes or practice exams to test your understanding.
Review Linear Algebra
Show steps
Review linear algebra to strengthen your foundation for understanding NLP algorithms and models that use linear algebra, such as word embeddings and neural networks.
Browse courses on
Linear Algebra
Show steps
-
Review basic linear algebra concepts such as vectors, matrices, and matrix operations.
-
Review linear algebra applications in NLP, such as dimensionality reduction and natural language understanding.
-
Practice solving linear algebra problems.
-
Complete online quizzes or practice exams to test your understanding.
Read 'Natural Language Processing with Python'
Show steps
Gain a comprehensive understanding of natural language processing concepts and techniques used in Course 2 by reading this book.
View
Natural Language Processing with Python:...
on Amazon
Show steps
-
Read the book thoroughly.
-
Take notes and highlight important concepts.
-
Complete the exercises at the end of each chapter.
-
Discuss the book with other students or a mentor.
Four other activities
Expand to see all activities and additional details
Show all seven activities
Autocorrect Practice Using Minimum Edit Distance
Show steps
Practice using minimum edit distance to correct misspelled words and improve your understanding of autocorrect algorithms.
Show steps
-
Find a dataset of misspelled words and their correct spellings.
-
Implement the minimum edit distance algorithm.
-
Use the algorithm to correct the misspelled words in the dataset.
-
Evaluate the accuracy of your algorithm.
-
Write a report on your findings.
POS Tagging Using Hidden Markov Models
Show steps
Practice using hidden Markov models to tag parts of speech in text and improve your understanding of natural language processing.
Browse courses on
Hidden Markov Models
Show steps
-
Find a dataset of text with part-of-speech tags.
-
Implement the Viterbi algorithm for hidden Markov models.
-
Use the algorithm to tag parts of speech in the dataset.
-
Evaluate the accuracy of your algorithm.
-
Write a report on your findings.
Autocomplete Language Model Using N-grams
Show steps
Create an autocomplete language model using n-grams to improve your understanding of language modeling and natural language processing.
Browse courses on
Autocomplete
Show steps
-
Find a dataset of text.
-
Implement an n-gram language model.
-
Use the model to autocomplete text.
-
Evaluate the accuracy of your model.
-
Write a report on your findings.
Word2Vec Model Using Neural Networks
Show steps
Create a word2vec model using neural networks to improve your understanding of word embeddings and natural language processing.
Browse courses on
Word2Vec
Show steps
-
Find a dataset of text.
-
Implement a neural network for word2vec.
-
Train the model on the dataset.
-
Use the model to generate word embeddings.
-
Write a report on your findings.
Review Probability and Statistics
Show steps
Review probability and statistics to strengthen your understanding of NLP concepts that rely on probabilistic models and statistical techniques.
Browse courses on
Probability
Show steps
Show steps
Show steps
- Review basic probability concepts such as conditional probability and Bayes' theorem.
- Review basic statistics concepts such as mean, variance, and standard deviation.
- Practice solving probability and statistics problems.
- Complete online quizzes or practice exams to test your understanding.
Review Linear Algebra
Show steps
Review linear algebra to strengthen your foundation for understanding NLP algorithms and models that use linear algebra, such as word embeddings and neural networks.
Browse courses on
Linear Algebra
Show steps
Show steps
Show steps
- Review basic linear algebra concepts such as vectors, matrices, and matrix operations.
- Review linear algebra applications in NLP, such as dimensionality reduction and natural language understanding.
- Practice solving linear algebra problems.
- Complete online quizzes or practice exams to test your understanding.
Read 'Natural Language Processing with Python'
Show steps
Gain a comprehensive understanding of natural language processing concepts and techniques used in Course 2 by reading this book.
View
Natural Language Processing with Python:...
on Amazon
Show steps
Show steps
Show steps
- Read the book thoroughly.
- Take notes and highlight important concepts.
- Complete the exercises at the end of each chapter.
- Discuss the book with other students or a mentor.
Four other activities
Expand to see all activities and additional details
Show all seven activities
Autocorrect Practice Using Minimum Edit Distance
Show steps
Practice using minimum edit distance to correct misspelled words and improve your understanding of autocorrect algorithms.
Show steps
Show steps
Show steps
- Find a dataset of misspelled words and their correct spellings.
- Implement the minimum edit distance algorithm.
- Use the algorithm to correct the misspelled words in the dataset.
- Evaluate the accuracy of your algorithm.
- Write a report on your findings.
POS Tagging Using Hidden Markov Models
Show steps
Practice using hidden Markov models to tag parts of speech in text and improve your understanding of natural language processing.
Browse courses on
Hidden Markov Models
Show steps
Show steps
Show steps
- Find a dataset of text with part-of-speech tags.
- Implement the Viterbi algorithm for hidden Markov models.
- Use the algorithm to tag parts of speech in the dataset.
- Evaluate the accuracy of your algorithm.
- Write a report on your findings.
Autocomplete Language Model Using N-grams
Show steps
Create an autocomplete language model using n-grams to improve your understanding of language modeling and natural language processing.
Browse courses on
Autocomplete
Show steps
Show steps
Show steps
- Find a dataset of text.
- Implement an n-gram language model.
- Use the model to autocomplete text.
- Evaluate the accuracy of your model.
- Write a report on your findings.
Word2Vec Model Using Neural Networks
Show steps
Create a word2vec model using neural networks to improve your understanding of word embeddings and natural language processing.
Browse courses on
Word2Vec
Show steps
Show steps
Show steps
- Find a dataset of text.
- Implement a neural network for word2vec.
- Train the model on the dataset.
- Use the model to generate word embeddings.
- Write a report on your findings.
Career center
Learners who complete Natural Language Processing with Probabilistic Models will develop knowledge and skills
that may be useful to these careers:
Software Engineer
Software Engineer
A Software Engineer uses engineering principles to create, deploy, maintain, and manage computer software. Whether a Software Engineer mostly writes code or manages others, knowledge of natural language processing is increasingly important as voice and text-based interfaces become more common. This course provides a foundation for understanding the probabilistic models and algorithms that are foundational to NLP.
Linguist
Linguist
A Linguist studies human language in all its forms, both spoken and written. Whether they study specific aspects of a single language or study the relationships between languages, machine learning and artificial intelligence are expanding the capabilities of linguists. This course helps build a foundation in the probabilistic models and algorithms that are used in computational linguistics.
Data Scientist
Data Scientist
A Data Scientist uses scientific principles to extract knowledge from data. This data can come from a variety of sources, from structured relational databases to unstructured text documents. Natural language processing is one of the most powerful ways to extract knowledge from text, making this course particularly relevant to Data Scientists who work with text data.
Machine Learning Engineer
Machine Learning Engineer
A Machine Learning Engineer uses machine learning to build predictive models. These models can be used for a variety of tasks, from recommending products to detecting fraud. Natural language processing is one of the most important applications of machine learning, so this course may be useful for Machine Learning Engineers.
Natural Language Processing Engineer
Natural Language Processing Engineer
A Natural Language Processing Engineer uses natural language processing to build systems that can understand and generate human language. These systems can be used for a variety of tasks, from machine translation to chatbots. This course provides a foundation for understanding the probabilistic models and algorithms that are used in NLP.
Computational Linguist
Computational Linguist
A Computational Linguist uses computer science to study human language. This field combines linguistics, computer science, and artificial intelligence to build systems that can understand and generate human language. This course provides a foundation for understanding the probabilistic models and algorithms that are used in computational linguistics.
Information Architect
Information Architect
An Information Architect designs and organizes information systems to make them easy to find and use. Natural language processing is increasingly important for organizing information systems, making this course may be useful for Information Architects.
User Experience Designer
User Experience Designer
A User Experience Designer designs and evaluates user interfaces to make them easy to use and enjoyable. Natural language processing is increasingly used to improve the user experience of text-based interfaces, making this course may be useful for User Experience Designers.
Content Writer
Content Writer
A Content Writer creates and edits written content for a variety of purposes, from marketing to journalism. Natural language processing is increasingly used to improve the quality and effectiveness of written content, making this course may be useful for Content Writers.
Technical Writer
Technical Writer
A Technical Writer creates and edits technical documentation, such as user manuals and white papers. Natural language processing is increasingly used to improve the quality and effectiveness of technical documentation, making this course may be useful for Technical Writers.
Editor
Editor
An Editor reviews and edits written content for a variety of purposes, from marketing to journalism. Natural language processing is increasingly used to improve the quality and effectiveness of written content, making this course may be useful for Editors.
Teacher
Teacher
A Teacher teaches students in a variety of settings, from elementary school to college. Natural language processing is increasingly used to improve the quality and effectiveness of education, making this course may be useful for Teachers.
Customer Service Representative
Customer Service Representative
A Customer Service Representative helps customers with their questions and concerns. Natural language processing is increasingly used to improve the quality and effectiveness of customer service, making this course may be useful for Customer Service Representatives.
Recruiter
Recruiter
A Recruiter finds and hires candidates for open positions. Natural language processing is increasingly used to improve the quality and effectiveness of recruiting, making this course may be useful for Recruiters.
Salesperson
Salesperson
A Salesperson sells products and services to customers. Natural language processing is increasingly used to improve the quality and effectiveness of sales, making this course may be useful for Salespeople.
For more career information including salaries, visit:
OpenCourser.com/course/gqnh0t/natural
Reading list
We've selected 11 books
that we think will supplement your
learning. Use these to
develop background knowledge, enrich your coursework, and gain a
deeper understanding of the topics covered in
Natural Language Processing with Probabilistic Models.
Must-have reference for NLP practitioners who use Python. It provides comprehensive documentation of the NLTK library, a powerful toolkit for NLP tasks in Python.
Save
Provides a comprehensive overview of deep learning, which powerful technique for NLP tasks. It covers topics such as neural networks, convolutional neural networks, and recurrent neural networks.
Provides a comprehensive overview of information theory, inference, and learning algorithms, which are fundamental concepts in NLP. It valuable resource for learners who want to gain a deeper understanding of the theoretical foundations of NLP.
Provides a comprehensive overview of pattern recognition and machine learning, which are fundamental concepts in NLP. It covers topics such as supervised learning, unsupervised learning, and reinforcement learning.
Provides in-depth coverage of machine learning algorithms and techniques that are specifically designed for NLP tasks, and many of the techniques in this book are the very same techniques that DeepLearning.AI uses in the NLP Specializations.
Provides a comprehensive overview of Bayesian reasoning and machine learning, which are important topics in NLP. It covers topics such as probability theory, Bayesian inference, and Markov chain Monte Carlo methods.
Speech and Language Processing comprehensive textbook that covers a wide range of topics in NLP, including fundamentals such as morphology, syntax, and semantics, as well as statistical and machine learning methods for NLP tasks such as text classification and language modeling. It valuable resource for both beginners and experienced NLP practitioners.
Provides a comprehensive overview of the mathematical foundations of machine learning, which is essential for understanding NLP algorithms and techniques. It covers topics such as linear algebra, calculus, and optimization.
Save
While the DeepLearning.AI NLP Specialization uses Python, this book provides a valuable introduction to text mining using R, another popular language for NLP tasks.
HMMs are an important topic in NLP, and are covered in the DeepLearning.AI NLP Specialization. provides additional depth on the theory and applications of HMMs.
Natural Language Processing with Python popular textbook that provides a practical introduction to NLP using Python. It covers a range of topics, including text preprocessing, feature engineering, and machine learning algorithms for NLP tasks. It good starting point for learners who are new to NLP and want to gain a hands-on understanding of the field.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/gqnh0t/natural
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Effort
At the rate of 5 hours a week, it typically takes 4 weeks to complete one Course.
Level
Intermediate
Via
Coursera
Institution
DeepLearning.AI
Instructors
Younes Bensouda Mourri
Łukasz Kaiser
Eddy Shyu
Language
English
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آذربایجان دیلی
This course belongs to a
collection
called
Natural Language Processing. It's comprised of four courses:
- Natural Language Processing with Attention Models
- Natural Language Processing with Classification and Vector Spaces
- Natural Language Processing with Probabilistic Models (viewing)
- Natural Language Processing with Sequence Models
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