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Axel Sirota
This course will teach you how to use word embeddings to use deep learning for NLP.
Read more
This course will teach you how to use word embeddings to use deep learning for NLP.
Read more
This course will teach you how to use word embeddings to use deep learning for NLP.
Natural language processing (NLP) is a set of tools and techniques that enables us to unlock the power of analyzing text. In this course, Implement Natural Language Processing for Word Embedding, you’ll learn how to use word embeddings to use neural networks for NLP. First, you’ll explore what word embeddings are and the most basic embedding: one hot encoding. Next, you’ll discover how to use word embeddings to do sentiment analysis. Finally, you’ll learn how to fine-tune existing word embeddings to improve your models as well as debase our embeddings for fairness. When you’re finished with this course, you’ll have the skills and knowledge of natural language processing needed to leverage word embeddings to create amazing NLP solutions with deep learning.
Register for this course and see more details by visiting:
OpenCourser.com/course/9j6ifr/implement
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What's inside
Syllabus
Course Overview
Why Process Text?
Training Word Representations
Fine-tuning Word Representations
Read more
Syllabus
Course Overview
Why Process Text?
Training Word Representations
Fine-tuning Word Representations
Read more
Good to know
Good to know
Know what's good ,
what to watch for , and
possible dealbreakers
Explores word embeddings, which are important and fundamental for NLP
Taught by experienced NLP instructor(s) Axel Sirota
Discusses deep learning, an important and highly sought after skill
Concepts are reinforced through hands-on exercises in addition to reading material
Requires students to have a basic understanding of NLP
Save this course
Save Implement Natural Language Processing for Word Embedding to your list so you can find it easily later:
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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 Implement Natural Language Processing for Word Embedding with these
activities:
Mentor another student in the course
Show steps
Solidify your knowledge by helping others.
Show steps
-
Identify a student who needs help
-
Meet regularly to provide guidance and support
-
Track your student's progress
Organize and review your notes
Show steps
Stay organized and enhance your retention by compiling and reviewing your notes.
Show steps
-
Gather all of your notes, assignments, and quizzes
-
Organize your materials by topic
-
Review your materials regularly
Read 'Natural Language Processing with Deep Learning'
Show steps
Expand your knowledge of word embeddings by reading this seminal book on the topic.
View
Deep Learning
on Amazon
Show steps
-
Gather background information on word embeddings
-
Read the book
-
Summarize the key concepts of the book
Four other activities
Expand to see all activities and additional details
Show all seven activities
Complete these tutorials on word embeddings
Show steps
Reinforce your understanding of the course materials by completing additional tutorials on word embeddings.
Show steps
-
Review available tutorials on word embeddings
-
Select 2-3 tutorials that cover different aspects of word embeddings
-
Complete the tutorials
Participate in a study group
Show steps
Reinforce your understanding of word embeddings by engaging with other students.
Show steps
-
Find a study group or create your own
-
Meet regularly to discuss course materials
-
Work together on practice problems
Practice using word embeddings
Show steps
Solidify your understanding of word embeddings by practicing different techniques.
Browse courses on
Natural Language Processing
Show steps
-
Identify different word embedding techniques
-
Implement these techniques in your own projects
-
Evaluate the results of your implementations
Build a word embedding model for a specific dataset
Show steps
Deepen your understanding of word embeddings by building your own model.
Browse courses on
Natural Language Processing
Show steps
-
Select a dataset for your model
-
Choose a word embedding technique
-
Train and evaluate your model
-
Write a report on your findings
Mentor another student in the course
Show steps
Solidify your knowledge by helping others.
Show steps
Show steps
Show steps
- Identify a student who needs help
- Meet regularly to provide guidance and support
- Track your student's progress
Organize and review your notes
Show steps
Stay organized and enhance your retention by compiling and reviewing your notes.
Show steps
Show steps
Show steps
- Gather all of your notes, assignments, and quizzes
- Organize your materials by topic
- Review your materials regularly
Read 'Natural Language Processing with Deep Learning'
Show steps
Expand your knowledge of word embeddings by reading this seminal book on the topic.
View
Deep Learning
on Amazon
Show steps
Show steps
Show steps
- Gather background information on word embeddings
- Read the book
- Summarize the key concepts of the book
Four other activities
Expand to see all activities and additional details
Show all seven activities
Complete these tutorials on word embeddings
Show steps
Reinforce your understanding of the course materials by completing additional tutorials on word embeddings.
Show steps
Show steps
Show steps
- Review available tutorials on word embeddings
- Select 2-3 tutorials that cover different aspects of word embeddings
- Complete the tutorials
Participate in a study group
Show steps
Reinforce your understanding of word embeddings by engaging with other students.
Show steps
Show steps
Show steps
- Find a study group or create your own
- Meet regularly to discuss course materials
- Work together on practice problems
Practice using word embeddings
Show steps
Solidify your understanding of word embeddings by practicing different techniques.
Browse courses on
Natural Language Processing
Show steps
Show steps
Show steps
- Identify different word embedding techniques
- Implement these techniques in your own projects
- Evaluate the results of your implementations
Build a word embedding model for a specific dataset
Show steps
Deepen your understanding of word embeddings by building your own model.
Browse courses on
Natural Language Processing
Show steps
Show steps
Show steps
- Select a dataset for your model
- Choose a word embedding technique
- Train and evaluate your model
- Write a report on your findings
Career center
Learners who complete Implement Natural Language Processing for Word Embedding will develop knowledge and skills
that may be useful to these careers:
Natural Language Processing Scientist
Natural Language Processing Scientist
The NLP Scientist uses natural language processing to build systems that understand human beings. This requires a strong foundation in both computer science and linguistics and is expected to hold at least a master's degree. The skills taught in this course can help you understand how neural networks are used to process words. This can be applied to improving search engines, automated assistants, sentiment analysis, and text classification.
Data Scientist
Data Scientist
The Data Scientist uses their knowledge of statistics, mathematics, computer science, and business intelligence to extract meaningful insights from data. The NLP skills a Data Scientist may use are often in the area of data mining, where the goal is to find insights in text-based data sources.
NLP Engineer
NLP Engineer
The NLP Engineer is responsible for designing, developing, and testing NLP models. This may include text classification, sentiment analysis, or question answering. This course can provide the NLP Engineer with the underpinnings of how neural networks, and specifically word embeddings, are used with NLP.
Machine Learning Engineer
Machine Learning Engineer
The Machine Learning Engineer is responsible for designing, developing, and testing machine learning models. This course can help a Machine Learning Engineer learn how to use word embeddings for natural language processing, which is a crucial skill for developing accurate models.
Software Engineer
Software Engineer
The Software Engineer is responsible for designing, developing, and testing software applications. This course can be helpful for a Software Engineer interested in natural language processing because it can provide a foundation in word embeddings.
Web Developer
Web Developer
The Web Developer is responsible for designing, developing, and testing web applications. This course may be useful for a Web Developer who wants to incorporate natural language processing into their applications.
Product Manager
Product Manager
The Product Manager is responsible for defining the product vision and roadmap. This course may be helpful for a Product Manager who wants to develop a new product or feature related to natural language processing.
Computational Linguist
Computational Linguist
The Computational Linguist may work on the development of natural language processing systems that understand human language. This course may be helpful to a Computational Linguist who wants to learn how to use word embeddings in NLP.
Natural Language Generation Engineer
Natural Language Generation Engineer
The Natural Language Generation Engineer may work on the development of natural language processing systems that generate text. This course may be helpful to a Natural Language Generation Engineer who wants to learn about the use of word embeddings in NLP.
Business Analyst
Business Analyst
The Business Analyst is responsible for gathering and analyzing business requirements. This course may be helpful for a Business Analyst who wants to learn more about natural language processing.
Technical Writer
Technical Writer
The Technical Writer is responsible for writing and editing user manuals, documentation, and other technical materials. This course may be helpful for a Technical Writer who wants to learn more about natural language processing.
User Experience Designer
User Experience Designer
The User Experience Designer is responsible for designing the user interface and user experience of websites, intranets, and other online systems. This course may be helpful for a User Experience Designer who wants to learn more about natural language processing.
Interaction Designer
Interaction Designer
The Interaction Designer is responsible for designing the interactions between humans and computers. This course may be helpful for an Interaction Designer who wants to learn more about natural language processing.
Information Architect
Information Architect
The Information Architect is responsible for designing and organizing the structure and content of websites, intranets, and other online systems. This course may be helpful for an Information Architect who wants to learn more about natural language processing.
Speech Recognition Engineer
Speech Recognition Engineer
The Speech Recognition Engineer may work on the development of natural language processing systmes that recognize speech. This course may be helpful for a Speech Recognition Engineer who wants to learn about using word embeddings in NLP.
For more career information including salaries, visit:
OpenCourser.com/course/9j6ifr/implement
Reading list
We've selected 12 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
Implement Natural Language Processing for Word Embedding.
Provides a comprehensive overview of natural language processing techniques, including word embeddings and deep learning.
Provides a comprehensive overview of the statistical foundations of NLP. It valuable resource for anyone who wants to learn more about the theoretical underpinnings of NLP.
Provides a comprehensive overview of NLP, including a wide range of NLP tasks and models. It valuable resource for anyone who wants to learn more about NLP.
Save
Provides a comprehensive overview of deep learning for NLP, including a wide range of NLP tasks and models. It valuable resource for anyone who wants to learn more about NLP.
Provides a detailed overview of machine learning techniques for natural language processing, including word embeddings and their applications.
Provides a detailed overview of deep learning techniques for natural language processing, including word embeddings and their applications.
Provides a comprehensive overview of speech and language processing, including both theoretical and practical aspects. It valuable resource for anyone who wants to learn more about NLP.
Provides a practical overview of natural language processing techniques, including word embeddings and their applications.
Provides a comprehensive overview of machine learning for text, including a wide range of NLP tasks and models. It valuable resource for anyone who wants to learn more about NLP.
Provides a comprehensive introduction to natural language processing (NLP) and how to use PyTorch for NLP tasks. It covers a range of topics, from word embeddings to deep learning models for NLP.
Provides a practical overview of natural language processing techniques using Python and NLTK, including word embeddings and their applications.
Provides a practical introduction to NLP, focusing on how to use NLP techniques to solve real-world problems. It covers a wide range of NLP tasks, including text classification, sentiment analysis, and machine translation.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/9j6ifr/implement
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Time
93 hours
Level
Introductory
Via
Pluralsight
Instructor
Axel Sirota
Language
English
Good to know
Explores word embeddings, which are important and fundamental for NLP
Taught by experienced NLP instructor(s) Axel Sirota
Discusses deep learning, an important and highly sought after skill
Concepts are reinforced through hands-on exercises in addition to reading material
Requires students to have a basic understanding of NLP
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