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

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.

This Specialization consists of four Courses. At the rate of 5 hours a week, it typically takes 4 weeks to complete each Course.

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.

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:

  • Trax: Neural Networks

  • Trax: Layers (Now Classes, subclasses, and inheritance)

  • Dense and ReLU layers

  • Serial Layer

  • Math in Simple RNNs

  • Gated Recurrent Units

  • RNNs and Vanishing Gradients

  • Introduction to LSTMs

  • LSTM Architecture

  • Triplets

• The following ungraded labs have been updated to new versions:

  • Hidden State Activation

  • GRU

  • Perplexity

  • Vanishing and Exploding gradients

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):

  • Seq2seq model for NMT

  • Seq2seq model with Attention

  • Queries, Keys, Values, and Attention

  • Setup for Machine Translation

  • Teacher Forcing

  • NMT with Attention

  • Evaluation: BLEU Score

  • Evaluation: ROUGE Score

  • Sampling and Decoding

  • Beam Search

  • Minimum Bayes Risk (MBR)

  • Transformers vs. RNNs

  • Transformers overview

  • Scaled dot-product Attention

  • Masked Self-Attention

  • Multi-Head Attention

• Four new lectures on Hugging Face have been added:

  • Introduction from the Hugging Face team

  • Introduction to Hugging Face

  • Using Transformers

  • Fine-tuning a pre-trained model

• Two new ungraded labs on Hugging Face have been added:

  • Use of pipelines for Question & Answering

  • Fine-Tuning a pre-trained model for Question & Answering

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.

• 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.

This course is completely online, so there’s no need to show up to a classroom in person. You can access your lectures, readings and assignments anytime and anywhere via the web or your mobile device.

If you subscribed, you get a 7-day free trial during which you can cancel at no penalty. After that, we don’t give refunds, but you can cancel your subscription at any time. See our full refund policy.

Yes! To get started, click the course card that interests you and enroll. You can enroll and complete the course to earn a shareable certificate, or you can audit it to view the course materials for free. When you subscribe to a course that is part of a Specialization, you’re automatically subscribed to the full Specialization. Visit your learner dashboard to track your progress.

Yes. In select learning programs, you can apply for financial aid or a scholarship if you can’t afford the enrollment fee. If fin aid or scholarship is available for your learning program selection, you’ll find a link to apply on the description page.

When you enroll in the course, you get access to all of the courses in the Specialization, and you earn a certificate when you complete the work. If you only want to read and view the course content, you can audit the course for free. If you cannot afford the fee, you can apply for financial aid.