Overview
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.
These and other NLP applications are going to be at the forefront of the coming transformation to an AI-powered future.
This Natural Language Processing Specialization offered by Coursera in partnership with Deeplearning 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.
Applied Learning Project
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!
Programme Structure
Courses included:
- Natural Language Processing with Classification and Vector Spaces
- Natural Language Processing with Probabilistic Models
- Natural Language Processing with Sequence Models
- Natural Language Processing with Attention Models
Key information
Duration
- Part-time
- 3 months
- 10 hrs/week
Start dates & application deadlines
Language
Delivered
- Self-paced
Campus Location
- Mountain View, United States
Disciplines
Data Science & Big Data Artificial Intelligence Machine Learning View 467 other Short Courses in Data Science & Big Data in United StatesWhat students do after studying
Academic requirements
We are not aware of any specific GRE, GMAT or GPA grading score requirements for this programme.
English requirements
We are not aware of any English requirements for this programme.
Other requirements
General requirements
Intermediate Level
- Working knowledge of machine learning, intermediate Python experience including DL frameworks & proficiency in calculus, linear algebra, & statistics
Tuition Fees
-
International Applies to you
Applies to youNon-residentsFree - Out-of-StateFree
Additional Details
Audit: free access to course materials except graded items|Certificate: a trusted way to showcase your skills|A year of unlimited access with Coursera Plus $199
Funding
Coursera provides financial aid to learners who cannot afford the fee. Apply for it by clicking on the Financial Aid link beneath the "Enroll" button on the left. You'll be prompted to complete an application and will be notified if you are approved. You'll need to complete this step for each course in the Specialization, including the Capstone Project.