Named Entity Recognition using LSTMs with Keras
This 1-hour long project-based course on Coursera's Rhyme platform will teach you how to 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. You will get instant access to a cloud desktop with Python, Jupyter, and Keras pre-installed, and you will be able to access the cloud desktop 5 times. This course is best suited for learners based in the North America region. Don't miss out on this opportunity to learn Named Entity Recognition using LSTMs with Keras! ▼
ADVERTISEMENT
Course Feature
Cost:
Paid
Provider:
Coursera
Certificate:
Paid Certification
Language:
English
Start Date:
17th Jul, 2023
Course Overview
❗The content presented here is sourced directly from Coursera platform. For comprehensive course details, including enrollment information, simply click on the 'Go to class' link on our website.
Updated in [August 31st, 2023]
Skills and Knowledge:
- Understand the fundamentals of natural language processing (NLP)
- Learn how to use the Keras API with TensorFlow as its backend to build and train a bidirectional LSTM neural network model
- Recognize named entities in text data
- Utilize the cloud desktop with Python, Jupyter, and Keras pre-installed
- Understand how to use the Rhyme platform for hands-on projects in the browser
Professional Growth:
This course on Named Entity Recognition using LSTMs with Keras contributes to professional growth in several ways:
1. Skill development: By completing this course, professionals can develop their skills in using the Keras API with TensorFlow to build and train neural network models. They will gain hands-on experience in implementing bidirectional LSTM models for named entity recognition, which is a valuable skill in the field of natural language processing.
2. Knowledge enhancement: Participants will learn about the concept of named entity recognition and its applications in information extraction and various natural language processing tasks such as machine translation, question answering, and text summarization. This knowledge can be applied to real-world projects and enhance their understanding of these domains.
3. Practical experience: The course is project-based, allowing professionals to work on a real-world problem and gain practical experience in solving it. They will have access to pre-configured cloud desktops with all the necessary software and data, enabling them to focus on learning and applying the concepts without the hassle of setting up the environment.
4. Accessibility and convenience: The course is hosted on Coursera's hands-on project platform called Rhyme, which provides a user-friendly and accessible learning experience. Professionals can access the course materials and instructions videos multiple times, allowing them to review and reinforce their understanding. The cloud desktops provided on Rhyme eliminate the need for local installations and provide a seamless learning experience.
Overall, this course equips professionals with valuable skills, knowledge, and practical experience in named entity recognition using LSTMs with Keras. It enhances their proficiency in natural language processing and opens up opportunities for them to apply these skills in various professional settings.
Further Education:
This course is suitable for preparing for further education. It covers the topic of Named Entity Recognition using LSTMs with Keras, which is a fundamental concept in natural language processing. The course also mentions that named entity recognition is a preprocessing step for many downstream NLP applications, indicating its relevance and importance in further education in the field. Additionally, the course provides hands-on projects on Coursera's platform, allowing learners to gain practical experience and apply their knowledge.
Course Provider
Provider Coursera's Stats at AZClass
Discussion and Reviews
0.0 (Based on 0 reviews)
Start your review of Named Entity Recognition using LSTMs with Keras