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Snehan Kekre
In this 1-hour long project-based course, you will use the Keras API with TensorFlow as its backend to build and train a bidirectional LSTM neural network model to recognize named entities in text data. Named entity recognition models can be used to identify...
Read more
In this 1-hour long project-based course, you will use the Keras API with TensorFlow as its backend to build and train a bidirectional LSTM neural network model to recognize named entities in text data. Named entity recognition models can be used to identify...
Read more
In this 1-hour long project-based course, you will use the Keras API with TensorFlow as its backend to build and train a bidirectional LSTM neural network model to recognize named entities in text data. Named entity recognition models can be used to identify mentions of people, locations, organizations, etc. Named entity recognition is not only a standalone tool for information extraction, but it also an invaluable preprocessing step for many downstream natural language processing applications like machine translation, question answering, and text summarization.
This course runs on Coursera's hands-on project platform called Rhyme. On Rhyme, you do projects in a hands-on manner in your browser. You will get instant access to pre-configured cloud desktops containing all of the software and data you need for the project. Everything is already set up directly in your internet browser so you can just focus on learning. For this project, you’ll get instant access to a cloud desktop with Python, Jupyter, and Keras pre-installed.
Notes:
- You will be able to access the cloud desktop 5 times. However, you will be able to access instructions videos as many times as you want.
- This course works best for learners who are based in the North America region. We’re currently working on providing the same experience in other regions.
Register for this course and see more details by visiting:
OpenCourser.com/course/pjs0ih/named
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possible dealbreakers
Useful for learners in the field of natural language processing or machine learning
Practical, hands-on approach with a cloud desktop
Focuses on building and training a bidirectional LSTM neural network
Teaches named entity recognition, an essential skill in language processing
Uses the popular Keras API, increasing accessibility for learners familiar with the framework
May be suitable for learners with an existing background in natural language processing or machine learning
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Reviews summary
Hands-on guide to named entity recognition with keras
Based on 40 reviews, this 1-hour project-based course is a good choice for learners wanting to build a named entity recognition model using Keras. The Rhyme interface is praised for its instant access to a cloud desktop with pre-installed software, while the guided project and clear explanations are mentioned in several positive reviews. However, it is important to note that some learners mention issues with the Rhyme interface being laggy and the course being too short or lacking theoretical explanations. Overall, the course is well-received, earning an average rating of 3.9 out of 5 stars.
The instructor's explanations are praised by many learners, who appreciate the clear and easy-to-follow manner in which the material is presented.
"As a guided project, the instructor explains most of the ideas used in examples in an easy-to-follow way."
"Good instructor."
"Great explanation "
Rhyme, the course's project platform, is praised for its ease of use and instant access to cloud desktops with pre-installed software, but some learners have reported issues with lag.
"Rhyme, you do projects in a hands-on manner in your browser."
"You’ll get instant access to a cloud desktop with Python, Jupyter, and Keras pre-installed."
"Rhyme interface is too laggy"
The course's guided project and end-to-end example of named entity recognition using Keras are praised by many learners, who appreciate the opportunity to gain hands-on experience.
"This project is a short end-end show."
"Wish to do more free courses."
"Good course covering all the basics required to train a NER model using LSTM..."
While the guided project and clear explanations are praised by many, some learners have reported a lack of theoretical explanations and in-depth discussions of functions and libraries.
"Needs more theoretical explanation alongside..."
"More in-depth explanations on model building and use of libraries could have been useful."
"The course content was very elementary..."
The course's 1-hour length is a concern for some learners, who felt it was too short and lacking in-depth explanations.
"it's too short..."
"Rhyme never connected and project was too simple."
"the course felt a bit too short."
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 Named Entity Recognition using LSTMs with Keras with these
activities:
Read and review 'Natural Language Processing with Python'
Show steps
This book will equip you with the foundational knowledge and skills in NLP that you'll be building upon throughout the course.
View
Natural Language Processing with Python:...
on Amazon
Show steps
-
Read Chapters 1-3.
-
Complete the exercises at the end of each chapter.
-
Create a summary of the key concepts covered in each chapter.
Review your Python programming skills.
Show steps
This course assumes a basic understanding of Python. Reviewing your Python skills will help you to succeed in this course.
Browse courses on
Python
Show steps
-
Take a Python refresher course.
-
Complete some Python practice exercises.
Review your machine learning knowledge.
Show steps
This course assumes a basic understanding of machine learning. Reviewing your machine learning knowledge will help you to succeed in this course.
Browse courses on
Machine Learning
Show steps
-
Take a machine learning refresher course.
-
Complete some machine learning practice exercises.
Five other activities
Expand to see all activities and additional details
Show all eight activities
Practice named entity recognition with spaCy
Show steps
spaCy is a powerful NLP library for Python. Practicing named entity recognition with spaCy will give you hands-on experience with the techniques and algorithms used in this project.
Browse courses on
Named Entity Recognition
Show steps
-
Install spaCy and download the English language model.
-
Load a sample text file.
-
Use spaCy to identify named entities in the text.
Watch the 'Named Entity Recognition with Keras' tutorial series
Show steps
These tutorials will provide you with a step-by-step guide on how to build and train a named entity recognition model with Keras.
Browse courses on
Named Entity Recognition
Show steps
-
Watch the first tutorial in the series.
-
Follow along with the code examples.
-
Complete the practice exercises.
Join a study group to discuss the course material.
Show steps
Discussing the course material with peers can help you clarify your understanding and identify areas where you need additional support.
Show steps
-
Find a study group or create your own.
-
Meet regularly to discuss the course material.
-
Work together to solve problems and answer questions.
Build a named entity recognition model with Keras
Show steps
This project will give you the opportunity to apply the concepts and techniques you'll learn in the course to a real-world problem.
Browse courses on
Named Entity Recognition
Show steps
-
Create a new Keras project.
-
Load the necessary data.
-
Preprocess the data.
-
Build and train a bidirectional LSTM model.
-
Evaluate the model's performance.
Contribute to the spaCy project.
Show steps
Contributing to the spaCy project will give you the opportunity to learn more about the inner workings of a real-world NLP library.
Browse courses on
spaCy
Show steps
-
Find an issue to work on.
-
Create a pull request.
-
Respond to feedback from the maintainers.
Read and review 'Natural Language Processing with Python'
Show steps
This book will equip you with the foundational knowledge and skills in NLP that you'll be building upon throughout the course.
View
Natural Language Processing with Python:...
on Amazon
Show steps
Show steps
Show steps
- Read Chapters 1-3.
- Complete the exercises at the end of each chapter.
- Create a summary of the key concepts covered in each chapter.
Review your Python programming skills.
Show steps
This course assumes a basic understanding of Python. Reviewing your Python skills will help you to succeed in this course.
Browse courses on
Python
Show steps
Show steps
Show steps
- Take a Python refresher course.
- Complete some Python practice exercises.
Review your machine learning knowledge.
Show steps
This course assumes a basic understanding of machine learning. Reviewing your machine learning knowledge will help you to succeed in this course.
Browse courses on
Machine Learning
Show steps
Show steps
Show steps
- Take a machine learning refresher course.
- Complete some machine learning practice exercises.
Five other activities
Expand to see all activities and additional details
Show all eight activities
Practice named entity recognition with spaCy
Show steps
spaCy is a powerful NLP library for Python. Practicing named entity recognition with spaCy will give you hands-on experience with the techniques and algorithms used in this project.
Browse courses on
Named Entity Recognition
Show steps
Show steps
Show steps
- Install spaCy and download the English language model.
- Load a sample text file.
- Use spaCy to identify named entities in the text.
Watch the 'Named Entity Recognition with Keras' tutorial series
Show steps
These tutorials will provide you with a step-by-step guide on how to build and train a named entity recognition model with Keras.
Browse courses on
Named Entity Recognition
Show steps
Show steps
Show steps
- Watch the first tutorial in the series.
- Follow along with the code examples.
- Complete the practice exercises.
Join a study group to discuss the course material.
Show steps
Discussing the course material with peers can help you clarify your understanding and identify areas where you need additional support.
Show steps
Show steps
Show steps
- Find a study group or create your own.
- Meet regularly to discuss the course material.
- Work together to solve problems and answer questions.
Build a named entity recognition model with Keras
Show steps
This project will give you the opportunity to apply the concepts and techniques you'll learn in the course to a real-world problem.
Browse courses on
Named Entity Recognition
Show steps
Show steps
Show steps
- Create a new Keras project.
- Load the necessary data.
- Preprocess the data.
- Build and train a bidirectional LSTM model.
- Evaluate the model's performance.
Contribute to the spaCy project.
Show steps
Contributing to the spaCy project will give you the opportunity to learn more about the inner workings of a real-world NLP library.
Browse courses on
spaCy
Show steps
Show steps
Show steps
- Find an issue to work on.
- Create a pull request.
- Respond to feedback from the maintainers.
Career center
Learners who complete Named Entity Recognition using LSTMs with Keras will develop knowledge and skills
that may be useful to these careers:
Natural Language Processing Engineer
Natural Language Processing Engineer
Natural Language Processing Engineers work on developing and implementing systems that can process and understand human language. This course may be particularly useful for those who wish to enter this field as it provides a foundation in natural language processing using deep learning techniques with Keras and TensorFlow. The hands-on experience gained in this course can be valuable for aspiring Natural Language Processing Engineers.
Academic Researcher
Academic Researcher
Academic Researchers conduct research in various fields, including natural language processing. This course may be particularly useful for those who wish to pursue research in this field as it provides a foundation in deep learning techniques with Keras and TensorFlow. The hands-on experience gained in this course can be valuable for Academic Researchers who want to apply their skills to real-world research projects.
Machine Learning Engineer
Machine Learning Engineer
Machine Learning Engineers work on real-world applications of machine learning, where they may build or refine algorithms. This course may be helpful for those who wish to enter the field as it provides hands-on experience in building a neural network model using TensorFlow and Keras. Additionally, the focus on natural language processing can be particularly relevant for Machine Learning Engineers working on text-based data.
Software Engineer
Software Engineer
Software Engineers design, build, and maintain software systems, and many work with natural language processing applications. This course may be helpful for Software Engineers who wish to gain experience in using Keras and TensorFlow for natural language processing tasks. The hands-on approach of the course can be valuable for Software Engineers who want to apply their skills to real-world projects.
Statistician
Statistician
Statisticians collect, analyze, and interpret data to help organizations make informed decisions. Many work with natural language processing to gain insights from text-based data. This course may be useful for Statisticians who wish to gain experience in using Keras and TensorFlow for natural language processing tasks.
Data Analyst
Data Analyst
Data Analysts collect, analyze, and interpret data to help organizations make informed decisions. Many work with natural language processing to gain insights from text-based data. This course may be useful for Data Analysts as it provides a foundation in natural language processing and the use of deep learning techniques with Keras and TensorFlow. The hands-on experience gained in this course can be valuable for Data Analysts who want to enhance their skills in this area.
Quantitative Analyst
Quantitative Analyst
Quantitative Analysts use mathematical and statistical techniques to analyze data and make predictions. Many work with natural language processing to gain insights from text-based data. This course may be useful for Quantitative Analysts who wish to gain experience in using Keras and TensorFlow for natural language processing tasks.
Business Analyst
Business Analyst
Business Analysts analyze business processes and systems to identify areas for improvement. Many work with natural language processing to gain insights from text-based data. This course may be useful for Business Analysts who wish to gain experience in using Keras and TensorFlow for natural language processing tasks.
Information Architect
Information Architect
Information Architects design and organize information systems to make them easy to find and use. Many work with natural language processing to improve the organization and accessibility of text-based information. This course may be useful for Information Architects who wish to gain experience in using Keras and TensorFlow for natural language processing tasks.
Data Scientist
Data Scientist
Data Scientists may be responsible for using deep learning and machine learning to obtain insights from data that can be critical to the growth of organizations. They may design and implement algorithms, conduct analysis, and use various statistical techniques. This course may be useful for aspiring Data Scientists as it provides a foundation in natural language processing, which can be valuable in a field that relies heavily on understanding data.
User Experience Designer
User Experience Designer
User Experience Designers focus on creating user interfaces that are both effective and enjoyable to use. Many work with natural language processing to improve the user experience of text-based interfaces. This course may be useful for User Experience Designers who wish to gain experience in using Keras and TensorFlow for natural language processing tasks.
Technical Writer
Technical Writer
Technical Writers create and maintain user guides, technical documentation, and other written materials. Many work with natural language processing to improve the clarity and accuracy of their writing. This course may be useful for Technical Writers who wish to gain experience in using Keras and TensorFlow for natural language processing tasks.
Content Strategist
Content Strategist
Content Strategists plan, create, and manage content for websites, social media, and other platforms. Many work with natural language processing to improve the quality and effectiveness of their content. This course may be useful for Content Strategists who wish to gain experience in using Keras and TensorFlow for natural language processing tasks.
Product Manager
Product Manager
Product Managers are responsible for the development and execution of product strategies. Aspiring Product Managers who are interested in products that involve natural language processing may find this course useful. The course provides a foundation in natural language processing using deep learning techniques, which can be valuable for understanding the technical aspects of such products.
Digital Marketing Specialist
Digital Marketing Specialist
Digital Marketing Specialists plan and execute digital marketing campaigns. Many work with natural language processing to improve the targeting and effectiveness of their campaigns. This course may be useful for Digital Marketing Specialists who wish to gain experience in using Keras and TensorFlow for natural language processing tasks.
For more career information including salaries, visit:
OpenCourser.com/course/pjs0ih/named
Reading list
We've selected 13 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
Named Entity Recognition using LSTMs with Keras.
Comprehensive overview of pattern recognition and machine learning, covering topics such as supervised learning, unsupervised learning, and Bayesian inference. It valuable resource for readers who want to gain a deep understanding of the theoretical foundations of pattern recognition and machine learning.
Classic textbook on speech and language processing, covering topics such as phonetics, phonology, morphology, syntax, semantics, and pragmatics. It valuable resource for readers who want to gain a deep understanding of the theoretical foundations of NLP.
Save
Comprehensive overview of deep learning, covering topics such as neural networks, convolutional neural networks, recurrent neural networks, and more. It valuable resource for readers who want to gain a deep understanding of the theoretical foundations of deep learning.
Provides a comprehensive overview of machine learning, covering topics such as probability theory, linear algebra, and optimization. It valuable resource for readers who want to gain a deep understanding of the theoretical foundations of machine learning.
Provides a comprehensive overview of Bayesian reasoning, covering topics such as probability theory, Bayesian inference, and Markov chain Monte Carlo. It valuable resource for readers who want to gain a deep understanding of the theoretical foundations of Bayesian reasoning.
Provides a comprehensive overview of reinforcement learning algorithms, covering topics such as value iteration, policy iteration, and Q-learning. It valuable resource for readers who want to gain a deep understanding of the theoretical foundations of reinforcement learning algorithms.
Comprehensive introduction to deep learning, covering the fundamentals of neural networks, convolutional neural networks, recurrent neural networks, and more. It valuable resource for readers who want to build a strong foundation in deep learning concepts.
Provides a comprehensive overview of computational linguistics, covering topics such as morphology, syntax, semantics, and pragmatics. It valuable resource for readers who want to gain a broad understanding of the theoretical foundations of NLP.
Comprehensive overview of reinforcement learning, covering topics such as Markov decision processes, value functions, and policy optimization. It valuable resource for readers who want to gain a deep understanding of the theoretical foundations of reinforcement learning.
Comprehensive overview of statistical learning, covering topics such as linear regression, logistic regression, and tree-based methods. It valuable resource for readers who want to gain a deep understanding of the theoretical foundations of statistical learning.
Provides a comprehensive overview of NLP techniques using PyTorch, covering topics such as data preprocessing, feature engineering, and model evaluation. It is particularly useful for readers who want to gain practical experience in building and deploying NLP models.
Provides a comprehensive overview of NLP techniques, covering topics such as text mining, machine learning, and deep learning. It valuable resource for readers who want to gain a broad understanding of NLP applications.
Comprehensive guide to the Natural Language Toolkit (NLTK), a popular Python library for NLP. It covers topics such as text processing, feature engineering, and machine learning. It valuable resource for readers who want to use NLTK for their NLP projects.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/pjs0ih/named
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Time
1 hours
Via
Coursera
Institutions
Rhyme
Coursera Project Network
Instructor
Snehan Kekre
Good to know
Useful for learners in the field of natural language processing or machine learning
Practical, hands-on approach with a cloud desktop
Focuses on building and training a bidirectional LSTM neural network
Teaches named entity recognition, an essential skill in language processing
Uses the popular Keras API, increasing accessibility for learners familiar with the framework
May be suitable for learners with an existing background in natural language processing or machine learning
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