Break into NLP. Master cutting-edge NLP techniques through four hands-on courses! Updated with the latest techniques in October '21.

4.6

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4,231 Bewertungen

Younes Bensouda Mourri +2 weitere Dozenten

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von

Spezialisierung Natural Language Processing (NPL)deeplearning.ai

Was Sie lernen werden

Use logistic regression, naïve Bayes, and word vectors to implement sentiment analysis, complete analogies & translate words.
Use dynamic programming, hidden Markov models, and word embeddings to implement autocorrect, autocomplete & identify part-of-speech tags for words.
Use recurrent neural networks, LSTMs, GRUs & Siamese networks in Trax for sentiment analysis, text generation & named entity recognition.
Use encoder-decoder, causal, & self-attention to machine translate complete sentences, summarize text, build chatbots & question-answering.

Kompetenzen, die Sie erwerben

Word2vec
Machine Translation
Sentiment Analysis
Transformers
Attention Models
Word Embeddings
Locality-Sensitive Hashing
Vector Space Models
Parts-of-Speech Tagging
N-gram Language Models
Autocorrect
Word Embedding

Über dieses Spezialisierung

59.062 kürzliche Aufrufe

Natural Language Processing (NLP) is a subfield of linguistics, computer science, and artificial intelligence that uses algorithms to interpret and manipulate human language. This technology 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. By the end of this Specialization, you will be ready to design NLP applications that perform question-answering and sentiment analysis, create tools to translate languages and summarize text, and even build chatbots. These and other NLP applications are going to be at the forefront of the coming transformation to an AI-powered future. 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.

Praktisches Lernprojekt

This Specialization will equip you with machine learning basics and state-of-the-art deep learning techniques needed to build cutting-edge NLP systems:

• 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 and Trax 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, question-answering, and to build chatbots. Learn T5, BERT, transformer, reformer, and more with 🤗 Transformers!

Zertifikat zur Vorlage

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Stufe „Mittel“

Working knowledge of machine learning, intermediate Python experience including DL frameworks & proficiency in calculus, linear algebra, & statistics

Etwa 4 Monate bis zum Abschluss

Empfohlenes Lerntempo: 8 Stunden/Woche

Englisch

Untertitel: Englisch, Japanisch

Zertifikat zur Vorlage

Erhalten Sie nach Abschluss ein Zertifikat

Kurse, die komplett online stattfinden

Beginnen Sie sofort und lernen Sie in Ihrem eigenen Tempo.

Flexibler Zeitplan

Festlegen und Einhalten flexibler Termine.

Stufe „Mittel“

Working knowledge of machine learning, intermediate Python experience including DL frameworks & proficiency in calculus, linear algebra, & statistics

Etwa 4 Monate bis zum Abschluss

Empfohlenes Lerntempo: 8 Stunden/Woche

Englisch

Untertitel: Englisch, Japanisch

So funktioniert die Spezialisierung

Kurse absolvieren

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Es gibt 4 Kurse in dieser Spezialisierung

Kurs1

Kurs 1

Natural Language Processing with Classification and Vector Spaces

4.6

Sterne

3,459 Bewertungen

•

700 Bewertungen

In Course 1 of the Natural Language Processing Specialization, you will:

a) Perform sentiment analysis of tweets using logistic regression and then naïve Bayes, b) Use vector space models to discover relationships between words and use PCA to reduce the dimensionality of the vector space and visualize those relationships, and c) Write a simple English to French translation algorithm using pre-computed word embeddings and locality-sensitive hashing to relate words via approximate k-nearest neighbor search. 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.

Kurs2

Kurs 2

Natural Language Processing with Probabilistic Models

4.7

Sterne

1,360 Bewertungen

•

239 Bewertungen

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.

Kurs3

Kurs 3

Natural Language Processing with Sequence Models

4.5

Sterne

893 Bewertungen

•

178 Bewertungen

In Course 3 of the Natural Language Processing Specialization, you will:

a) Train a neural network with GLoVe word embeddings to perform sentiment analysis of tweets, b) Generate synthetic Shakespeare text using a Gated Recurrent Unit (GRU) language model, c) Train a recurrent neural network to perform named entity recognition (NER) using LSTMs with linear layers, and d) Use so-called ‘Siamese’ LSTM models to compare questions in a corpus and identify those that are worded differently but have the same meaning. 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.

Kurs4

Kurs 4

Natural Language Processing with Attention Models

4.3

Sterne

761 Bewertungen

•

187 Bewertungen

In Course 4 of the Natural Language Processing Specialization, you will:

a) Translate complete English sentences into German using an encoder-decoder attention model, b) Build a Transformer model to summarize text, c) Use T5 and BERT models to perform question-answering, and d) Build a chatbot using a Reformer 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! Learners should have a working knowledge of machine learning, intermediate Python including experience with a deep learning framework (e.g., TensorFlow, Keras), as well as proficiency in calculus, linear algebra, and statistics. Please make sure that you’ve completed course 3 - Natural Language Processing with Sequence Models - before starting this course. 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.

Dozenten

von

deeplearning.ai

DeepLearning.AI is an education technology company that develops a global community of AI talent.

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What is Natural Language Processing (NLP)?
Natural language processing is a subfield of linguistics, computer science, and artificial intelligence that uses algorithms to interpret and manipulate human language.
What is the Natural Language Processing Specialization about?
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, summarize text, and even build chatbots. 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.
What will I be able to do after completing the Natural Language Processing Specialization?
This Specialization will equip you with both the machine learning basics as well as the state-of-the-art deep learning techniques needed to build cutting-edge NLP systems:
• 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 and Trax 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, question-answering, and build chatbots. Learn include T5, BERT, transformer, reformer, and more!
What makes this Specialization so different?
The Natural Language Processing Specialization is one-of-a-kind. • It teaches cutting-edge techniques drawn from recent academic papers, some of which were only first published in 2019. • It covers practical methods for handling common NLP use cases (autocorrect, autocomplete), as well as advanced deep learning techniques for chatbots and question-answering. • It starts with the foundations and takes you to a stage where you can build state-of-the-art attention models that allow for parallel computing. • You will not only use packages but also learn how to build these models from scratch. We walk you through all the steps, from data processing to the finished products you can use in your own projects. • You will complete one project every week to make sure you understand the concepts for a total of 16 programming assignments.
What background knowledge is necessary for the Natural Language Processing Specialization?
Working knowledge of machine learning, intermediate Python experience including DL frameworks & proficiency in calculus, linear algebra, & stats.
Who is this Specialization for?
This Specialization is for students of machine learning or artificial intelligence and software engineers looking for a deeper understanding of how NLP models work and how to apply them.
How long does it take to complete the Natural Language Processing Specialization?
This Specialization consists of four Courses. At the rate of 5 hours a week, it typically takes 4 weeks to complete each Course.
Who created the Natural Language Processing Specialization?
Younes Bensouda Mourri and Lukasz Kaiser created the Natural Language Processing Specialization.
Younes Bensouda Mourricompleted his Bachelor's in Applied Math and CS and Master's in Statistics from Stanford University. Younes helped create 3 AI courses at Stanford - Applied Machine Learning, Deep Learning, and Teaching AI - and taught two for a few years. Currently, he is an adjunct lecturer of computer science at Stanford University.
Lukasz Kaiser is a Staff Research Scientist at Google Brain and the co-author of Tensorflow, the Tensor2Tensor and Trax libraries, and the “Attention is all you need” Transformer paper.
The Natural Language Processing Specialization was updated in October 2021. What is different in the new version?
In Course 1: NLP with Classification and Vector Spaces,
All the programming assignments and ungraded labs have been refactored
All programming assignments have new automatic graders
In Course 2: NLP with Probabilistic Models,
All the programming assignments and ungraded labs have been refactored
All programming assignments have new automatic graders
In Course 3: NLP with Sequence Models,
A new section on visualizing embeddings from the trained model has been added to the Week 1 assignment
Corrections have been made to the evaluation section of the Week 2 assignment Week 3 assignment has been refactored
The following lectures have been updated to new versions:
The following ungraded labs have been updated to new versions:
In Course 4: NLP with Attention Models,
Two new ungraded labs concerned with how attention is implemented in deep learning models have been added
The following lectures have been updated to new versions or modified (excluding HuggingFace content):
Four new lectures on Hugging Face have been added:
Two new ungraded labs on Hugging Face have been added:
I’m currently enrolled in one or more courses in the Natural Language Processing Specialization. What does this mean for me?
If you would like to update to the new material, reset your deadlines. If you’re in the middle of a course, you will lose your progress when you reset your deadlines. Please save any notebook assignments you've submitted by downloading your existing notebooks before switching to the new version.
• Your certificates will carry over for any courses you’ve already completed.
• If your subscription is currently active, you can access the updated labs and submit assignments without paying for the month again.
• If you go to the Specialization, you will see the original version of the lecture videos and assignments. You can complete the original version if so desired (this is not recommended).
• If you would like to update to the new material, reset your deadlines. If you’re in the middle of a course, you will lose your notebook work when you reset your deadlines.• Please save your work by downloading your existing notebooks before switching to the new version.
• If you do not see the option to reset deadlines, contact Coursera via the Learner Help Center.
I’ve already completed one or more courses in the Natural Language Processing Specialization but don’t have an active subscription. What does this mean for me?
• Your certificates will carry over for any courses you’ve already completed. • If your subscription is currently inactive, you will need to pay again to access the labs and submit assignments for those courses.

Haben Sie weitere Fragen? Besuchen Sie das Learner Help Center.