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Jay Alammar,
Arpan Chakraborty,
Luis Serrano,
and
Dana Sheahen
Boost your AI skills with Udacity's Natural Language Processing Training Course. Learn voice user interface techniques, build speech recognition models and more.
Prerequisite details
Read more
Boost your AI skills with Udacity's Natural Language Processing Training Course. Learn voice user interface techniques, build speech recognition models and more.
Prerequisite details
Read more
Boost your AI skills with Udacity's Natural Language Processing Training Course. Learn voice user interface techniques, build speech recognition models and more.
Prerequisite details
To optimize your success in this program, we've created a list of prerequisites and recommendations to help you prepare for the curriculum. Prior to enrolling, you should have the following knowledge:
- Intermediate Python
- Neural network basics
- Deep learning framework proficiency
- Basic probability
- Object-oriented programming basics
You will also need to be able to communicate fluently and professionally in written and spoken English.
Register for this course and see more details by visiting:
OpenCourser.com/course/fyaoqo/computing
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What's inside
Syllabus
An introduction of the course outline and prerequisite.
Transform text using methods like Bag-of-Words, TF-IDF, Word2Vec and GloVE to extract features that you can use in machine learning models.
Read more
Syllabus
An introduction of the course outline and prerequisite.
Transform text using methods like Bag-of-Words, TF-IDF, Word2Vec and GloVE to extract features that you can use in machine learning models.
Read more
In this section, you'll learn to split a collection of documents into topics using Latent Dirichlet Analysis (LDA). In the lab, you'll be able to apply this model to a dataset of news articles.
Learn about using several machine learning classifiers, including Recurrent Neural Networks, to predict the sentiment in text. Apply this to a dataset of movie reviews.
Here you'll learn about a specific architecture of RNNs for generating one sequence from another sequence. These RNNs are useful for chatbots, machine translation, and more!
Attention is one of the most important recent innovations in deep learning. In this section, you'll learn attention, and you'll go over a basic implementation of it in the lab.
This section will prepare you for the Machine Translation project. Here you will get hands-on practice with RNNs in Keras.
Apply the skills you've learned in Natural Language Processing to the challenging and extremely rewarding task of Machine Translation.
Good to know
Good to know
Know what's good ,
what to watch for , and
possible dealbreakers
Taught by researchers, industry experts, and recognized innovators in the field
Focuses on in-demand skills in the growing field of natural language processing
Provides hands-on practice through interactive labs and exercises
Requires strong foundational knowledge in Python, neural networks, and deep learning
Save this course
Save Computing With Natural Language to your list so you can find it easily later:
Save
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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 Computing With Natural Language with these
activities:
Read 'Natural Language Processing with Python' by Steven Bird, Ewan Klein, and Edward Loper
Show steps
Supplement your course learning with this comprehensive book to expand your understanding of NLP concepts, techniques, and implementations.
View
Natural Language Processing with Python:...
on Amazon
Show steps
-
Read through specific chapters relevant to the course material
-
Complete exercises and examples provided in the book
-
Refer back to the book for additional clarification and reference
Form study groups for collaborative review and concept clarification
Show steps
Connect with peers and engage in collaborative learning, enhancing your understanding through shared discussions and insights.
Show steps
-
Find classmates with similar schedules and interests
-
Schedule regular study sessions
-
Prepare discussion topics and questions
Practice implementing Bag-of-Words and TF-IDF feature extraction techniques
Show steps
Practice the fundamental NLP techniques of Bag-of-Words and TF-IDF feature extraction to enhance your understanding of text representation.
Browse courses on
Feature Extraction
Show steps
-
Review the Bag-of-Words and TF-IDF concepts
-
Find a dataset of documents
-
Extract features using Bag-of-Words and TF-IDF
-
Visualize the extracted features
Five other activities
Expand to see all activities and additional details
Show all eight activities
Create a visualization to illustrate Latent Dirichlet Allocation (LDA)
Show steps
Reinforce your understanding of LDA by visually representing how it identifies topics in text.
Browse courses on
Latent Dirichlet Allocation
Show steps
-
Choose a dataset of documents
-
Apply LDA to identify topics
-
Visualize the topics and their distribution
Develop a presentation on NLP applications in a specific industry
Show steps
Research and present on NLP applications in an industry of your interest, broadening your knowledge and gaining insights into real-world uses of NLP.
Browse courses on
NLP Applications
Show steps
-
Choose an industry and research its NLP applications
-
Gather data and examples to support your presentation
-
Create visual aids and slides
Build a sentiment analysis model using Recurrent Neural Networks (RNNs)
Show steps
Apply your knowledge of RNNs to build a practical sentiment analysis model, enhancing your ability to analyze text sentiment.
Browse courses on
Sentiment Analysis
Show steps
-
Collect and prepare a dataset of movie reviews
-
Build an RNN model for sentiment analysis
-
Train and evaluate the model
Explore tutorials on Attention Mechanisms in Deep Learning
Show steps
Deepen your knowledge of Attention Mechanisms by following tutorials and implementing them in your own code.
Browse courses on
Attention Mechanisms
Show steps
-
Find tutorials or resources on Attention Mechanisms
-
Implement Attention Mechanisms in your code
Build a Machine Translation system using RNNs
Show steps
Challenge yourself with a Machine Translation project to solidify your understanding of NLP techniques and their application in real-world scenarios.
Browse courses on
Machine Translation
Show steps
-
Gather a parallel corpus for training
-
Preprocess and tokenize the data
-
Build an RNN-based Machine Translation system
Read 'Natural Language Processing with Python' by Steven Bird, Ewan Klein, and Edward Loper
Show steps
Supplement your course learning with this comprehensive book to expand your understanding of NLP concepts, techniques, and implementations.
View
Natural Language Processing with Python:...
on Amazon
Show steps
Show steps
Show steps
- Read through specific chapters relevant to the course material
- Complete exercises and examples provided in the book
- Refer back to the book for additional clarification and reference
Form study groups for collaborative review and concept clarification
Show steps
Connect with peers and engage in collaborative learning, enhancing your understanding through shared discussions and insights.
Show steps
Show steps
Show steps
- Find classmates with similar schedules and interests
- Schedule regular study sessions
- Prepare discussion topics and questions
Practice implementing Bag-of-Words and TF-IDF feature extraction techniques
Show steps
Practice the fundamental NLP techniques of Bag-of-Words and TF-IDF feature extraction to enhance your understanding of text representation.
Browse courses on
Feature Extraction
Show steps
Show steps
Show steps
- Review the Bag-of-Words and TF-IDF concepts
- Find a dataset of documents
- Extract features using Bag-of-Words and TF-IDF
- Visualize the extracted features
Five other activities
Expand to see all activities and additional details
Show all eight activities
Create a visualization to illustrate Latent Dirichlet Allocation (LDA)
Show steps
Reinforce your understanding of LDA by visually representing how it identifies topics in text.
Browse courses on
Latent Dirichlet Allocation
Show steps
Show steps
Show steps
- Choose a dataset of documents
- Apply LDA to identify topics
- Visualize the topics and their distribution
Develop a presentation on NLP applications in a specific industry
Show steps
Research and present on NLP applications in an industry of your interest, broadening your knowledge and gaining insights into real-world uses of NLP.
Browse courses on
NLP Applications
Show steps
Show steps
Show steps
- Choose an industry and research its NLP applications
- Gather data and examples to support your presentation
- Create visual aids and slides
Build a sentiment analysis model using Recurrent Neural Networks (RNNs)
Show steps
Apply your knowledge of RNNs to build a practical sentiment analysis model, enhancing your ability to analyze text sentiment.
Browse courses on
Sentiment Analysis
Show steps
Show steps
Show steps
- Collect and prepare a dataset of movie reviews
- Build an RNN model for sentiment analysis
- Train and evaluate the model
Explore tutorials on Attention Mechanisms in Deep Learning
Show steps
Deepen your knowledge of Attention Mechanisms by following tutorials and implementing them in your own code.
Browse courses on
Attention Mechanisms
Show steps
Show steps
Show steps
- Find tutorials or resources on Attention Mechanisms
- Implement Attention Mechanisms in your code
Build a Machine Translation system using RNNs
Show steps
Challenge yourself with a Machine Translation project to solidify your understanding of NLP techniques and their application in real-world scenarios.
Browse courses on
Machine Translation
Show steps
Show steps
Show steps
- Gather a parallel corpus for training
- Preprocess and tokenize the data
- Build an RNN-based Machine Translation system
Career center
Learners who complete Computing With Natural Language will develop knowledge and skills
that may be useful to these careers:
Natural Language Processing Engineer
Natural Language Processing Engineer
Natural Language Processing Engineers are responsible for developing and maintaining natural language processing systems. These systems can be used for a variety of purposes, such as machine translation, spam filtering, and search engine optimization. This course will provide you with the skills you need to succeed in this role, as you will build a comprehensive knowledge of the fundamentals of Natural Language Processing.
Data Scientist
Data Scientist
A Data Scientist is a data professional who can manage large amounts of data and turn it into useful information. They use their knowledge of statistics, machine learning, and other data science techniques to extract insights from data. With roles becoming increasingly specialized, one popular specialization is in Natural Language Processing. This course will provide you with the essential skills for this role by helping you build a foundation in this increasingly important subfield.
Machine Learning Engineer
Machine Learning Engineer
Machine Learning Engineers are computer scientists who build, test, and deploy machine learning models. These models can be used for a variety of purposes, such as predicting customer behavior, detecting fraud, and recommending products. This course will provide you with the skills you need to succeed in this role, as a strong foundation in Natural Language Processing is increasingly required for more specialized roles within the field.
Software Engineer
Software Engineer
Software Engineers design, develop, and maintain software systems. They use their knowledge of programming languages and software development tools to create software that meets the needs of users. This course may be useful for aspiring Software Engineers who wish to specialize in Natural Language Processing, as it provides a strong foundation in this rapidly growing field.
Data Analyst
Data Analyst
Data Analysts are responsible for collecting, cleaning, and analyzing data. They use their skills in statistics and data analysis to identify trends and patterns in data. This course may be useful for an aspiring Data Analyst who wishes to focus on Natural Language Processing, a popular specialization that allows analysts to focus on increasingly complex data sets that include text.
Technical Writer
Technical Writer
Technical Writers are responsible for creating documentation for software and other technical products. They use their knowledge of writing and technology to create clear and concise documentation that helps users understand how to use products. This course may be useful for aspiring Technical Writers who wish to focus on Natural Language Processing, as it provides a strong foundation in this rapidly growing field.
Business Development Manager
Business Development Manager
Business Development Managers are responsible for developing and executing business strategies. They work with customers and partners to identify and develop new business opportunities. This course may be useful for aspiring Business Development Managers who wish to focus on Natural Language Processing, as it provides a strong foundation in this rapidly growing field.
Market Researcher
Market Researcher
Market Researchers are responsible for collecting and analyzing data about customers and markets. They use their skills to identify trends and opportunities. This course may be useful for aspiring Market Researchers who wish to focus on Natural Language Processing, as it provides a strong foundation in this rapidly growing field.
Sales Engineer
Sales Engineer
Sales Engineers are responsible for selling technical products and services. They use their knowledge of technology and sales to help customers understand and buy products. This course may be useful for aspiring Sales Engineers who wish to focus on Natural Language Processing, as it provides a strong foundation in this rapidly growing field.
Product Manager
Product Manager
Product Managers are responsible for planning and managing the development of products. They work with engineers, designers, and other stakeholders to ensure that products meet the needs of users. This course may be useful for aspiring Product Managers who wish to focus on Natural Language Processing, as it provides a strong foundation in this rapidly growing field.
Business Analyst
Business Analyst
Business Analysts are responsible for understanding the business needs of an organization and translating those needs into technical requirements. They use their knowledge of business processes and technology to help organizations improve their operations. This course may be useful for aspiring Business Analysts who wish to focus on Natural Language Processing, as it provides a strong foundation in this rapidly growing field.
Operations Research Analyst
Operations Research Analyst
Operations Research Analysts are responsible for using mathematical and statistical models to solve business problems. They use their skills to improve efficiency and productivity. This course may be useful for aspiring Operations Research Analysts who wish to focus on Natural Language Processing, as it provides a strong foundation in this rapidly growing field.
Project Manager
Project Manager
Project Managers are responsible for planning, executing, and closing projects. They work with teams of people to achieve project goals. This course may be useful for aspiring Project Managers who wish to focus on Natural Language Processing, as it provides a strong foundation in this rapidly growing field.
Quantitative Analyst
Quantitative Analyst
Quantitative Analysts are responsible for using mathematical and statistical models to analyze financial data. They use their skills to identify investment opportunities and make trading decisions. This course may be useful for aspiring Quantitative Analysts who wish to focus on Natural Language Processing, as it provides a strong foundation in this rapidly growing field.
For more career information including salaries, visit:
OpenCourser.com/course/fyaoqo/computing
Reading list
We've selected ten 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
Computing With Natural Language.
This leading textbook in the field of speech and language processing and could serve to replace the course's current curriculum with additional depth and detail.
Save
While this book does not focus solely on natural language processing, it is one of the definitive works on deep learning in general and would serve as an excellent resource for the learning objectives laid out for the course.
Provides a good practical guide to deep learning using the Fastai library, which is built on top of PyTorch.
This classic book provides a very good foundational review of reinforcement learning.
Would help supplement the course with additional material on pattern recognition and machine learning.
Provides a good overview of practical machine learning which would complement the theoretical material covered by the course.
While focusing primarily on statistical learning, this book provides a good ancillary reference to the machine learning material covered by the course.
Is often used at the undergraduate and graduate level to give a foundational introduction to natural language processing. This will supplement the topics covered by the class with additional breadth and serve as a good reference book.
Save
Good reference for the prerequisite material on machine learning that the course assumes.
Is good for getting background on numerical methods, such as the EM algorithm, which is used frequently in statistical NLP.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/fyaoqo/computing
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Effort
4 weeks
Level
Advanced
Via
Udacity
Instructors
Jay Alammar
Arpan Chakraborty
Luis Serrano
Dana Sheahen
Language
English
Good to know
Taught by researchers, industry experts, and recognized innovators in the field
Focuses on in-demand skills in the growing field of natural language processing
Provides hands-on practice through interactive labs and exercises
Requires strong foundational knowledge in Python, neural networks, and deep learning
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