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Mohammed Murtuza Qureshi
In this 2-hour long project-based course, you will learn how to do text classification use pre-trained Word Embeddings and Long Short Term Memory (LSTM) Neural Network using the Deep Learning Framework of Keras and Tensorflow in Python. We will be using...
Read more
In this 2-hour long project-based course, you will learn how to do text classification use pre-trained Word Embeddings and Long Short Term Memory (LSTM) Neural Network using the Deep Learning Framework of Keras and Tensorflow in Python. We will be using...
Read more
In this 2-hour long project-based course, you will learn how to do text classification use pre-trained Word Embeddings and Long Short Term Memory (LSTM) Neural Network using the Deep Learning Framework of Keras and Tensorflow in Python. We will be using Google Colab for writing our code and training the model using the GPU runtime provided by Google on the Notebook. We will first train a Word2Vec model and use its output in the embedding layer of our Deep Learning model LSTM which will then be evaluated for its accuracy and loss on unknown data and tested on few samples.
Note: 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/xyqrqc/text
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possible dealbreakers
For those seeking an edge in understanding innovation and groundbreaking topics
Good for Students interested in learning Text Classification, Word Embeddings, LSTM Neural Networks, Deep Learning, Keras, and Tensorflow
Provides hands-on experience with Word2Vec modeling, LSTM model development, and evaluation
Suitable for learners with basic knowledge of Python and machine learning concepts
It is unclear if this course is accessible to learners outside of North America
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Reviews summary
Valuable lstm model-building guide
This project-based course is a great resource for learning how to do text classification using pre-trained Word Embeddings and Long Short Term Memory (LSTM) Neural Network. It provides a hands-on approach to building an LSTM model using Keras and Tensorflow in Python in a Google Colab environment.
Skip manual data splitting
"...the whole project can be shortened if we skip the part on manually splitting the data into train, test and validation steps."
Hands-on project
"This project-based course is a great resource for learning how to do text classification..."
"...great project worth the time..."
Best for North America
"Note: 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."
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 Text Classification Using Word2Vec and LSTM on Keras with these
activities:
Organize your notes and assignments
Show steps
Improve your organization and study habits, making it easier to review and retain information from the course.
Show steps
-
Gather your notes, assignments, and other course materials.
-
Organize them into a logical structure.
-
Review your materials regularly.
Review Natural Language Processing with Python
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Get a strong foundation in the fundamentals of natural language processing and how to use the Natural Language Toolkit (NLTK) library in Python.
View
Natural Language Processing with Python:...
on Amazon
Show steps
-
Read chapters 1-3 of the book.
-
Complete the practice exercises in chapters 1-3.
-
Research additional resources on NLTK.
Review Deep Learning with Python
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Get a strong foundation in the fundamentals of deep learning and how to use the Keras library in Python.
View
Deep Learning with Python, Second Edition
on Amazon
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-
Read chapters 1-4 of the book.
-
Complete the practice exercises in chapters 1-4.
-
Research additional resources on Keras.
Five other activities
Expand to see all activities and additional details
Show all eight activities
Practice text classification problems
Show steps
Improve your understanding of text classification algorithms and gain proficiency in implementing them in Python.
Browse courses on
Text Classification
Show steps
-
Solve practice problems on Kaggle or a similar platform.
-
Implement text classification algorithms from scratch in Python.
Follow tutorials on LSTM neural networks
Show steps
Develop a deeper understanding of the architecture and implementation of LSTM neural networks.
Browse courses on
LSTM
Show steps
-
Find tutorials on LSTM neural networks.
-
Follow the tutorials and implement LSTM neural networks in Python.
Create a blog post or article on text classification
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Solidify your understanding of text classification concepts and improve your communication skills.
Browse courses on
Text Classification
Show steps
-
Choose a topic related to text classification.
-
Research the topic and gather information.
-
Write a blog post or article.
Build a text classification web application
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Apply your knowledge of text classification to a real-world problem and gain experience in web development.
Browse courses on
Text Classification
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-
Design the web application.
-
Implement the web application.
-
Deploy the web application.
Contribute to an open-source text classification project
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Gain hands-on experience in contributing to open-source projects and further your understanding of text classification.
Browse courses on
Text Classification
Show steps
-
Find an open-source text classification project.
-
Identify an area where you can contribute.
-
Submit a pull request.
Organize your notes and assignments
Show steps
Improve your organization and study habits, making it easier to review and retain information from the course.
Show steps
Show steps
Show steps
- Gather your notes, assignments, and other course materials.
- Organize them into a logical structure.
- Review your materials regularly.
Review Natural Language Processing with Python
Show steps
Get a strong foundation in the fundamentals of natural language processing and how to use the Natural Language Toolkit (NLTK) library in Python.
View
Natural Language Processing with Python:...
on Amazon
Show steps
Show steps
Show steps
- Read chapters 1-3 of the book.
- Complete the practice exercises in chapters 1-3.
- Research additional resources on NLTK.
Review Deep Learning with Python
Show steps
Get a strong foundation in the fundamentals of deep learning and how to use the Keras library in Python.
View
Deep Learning with Python, Second Edition
on Amazon
Show steps
Show steps
Show steps
- Read chapters 1-4 of the book.
- Complete the practice exercises in chapters 1-4.
- Research additional resources on Keras.
Five other activities
Expand to see all activities and additional details
Show all eight activities
Practice text classification problems
Show steps
Improve your understanding of text classification algorithms and gain proficiency in implementing them in Python.
Browse courses on
Text Classification
Show steps
Show steps
Show steps
- Solve practice problems on Kaggle or a similar platform.
- Implement text classification algorithms from scratch in Python.
Follow tutorials on LSTM neural networks
Show steps
Develop a deeper understanding of the architecture and implementation of LSTM neural networks.
Browse courses on
LSTM
Show steps
Show steps
Show steps
- Find tutorials on LSTM neural networks.
- Follow the tutorials and implement LSTM neural networks in Python.
Create a blog post or article on text classification
Show steps
Solidify your understanding of text classification concepts and improve your communication skills.
Browse courses on
Text Classification
Show steps
Show steps
Show steps
- Choose a topic related to text classification.
- Research the topic and gather information.
- Write a blog post or article.
Build a text classification web application
Show steps
Apply your knowledge of text classification to a real-world problem and gain experience in web development.
Browse courses on
Text Classification
Show steps
Show steps
Show steps
- Design the web application.
- Implement the web application.
- Deploy the web application.
Contribute to an open-source text classification project
Show steps
Gain hands-on experience in contributing to open-source projects and further your understanding of text classification.
Browse courses on
Text Classification
Show steps
Show steps
Show steps
- Find an open-source text classification project.
- Identify an area where you can contribute.
- Submit a pull request.
Career center
Learners who complete Text Classification Using Word2Vec and LSTM on Keras will develop knowledge and skills
that may be useful to these careers:
Natural Language Processing Engineer
Natural Language Processing Engineer
A Natural Language Processing Engineer develops and maintains natural language processing models used in a variety of industries. Some of the tasks a Natural Language Processing Engineer may perform include gathering and cleaning text data, developing and training natural language processing models, and evaluating and deploying models into production. This course may be useful for someone looking to become a Natural Language Processing Engineer because it provides a foundation in the use of Word2Vec and LSTM for text classification, two important techniques in the field of natural language processing.
Machine Learning Engineer
Machine Learning Engineer
A Machine Learning Engineer builds and maintains machine learning models used in a variety of industries. Some of the tasks a Machine Learning Engineer may perform include gathering and cleaning data, developing and training machine learning models, and evaluating and deploying models into production. This course may be useful for someone looking to become a Machine Learning Engineer because it provides a foundation in the use of Word2Vec and LSTM for text classification, two important techniques in the field of machine learning.
Data Analyst
Data Analyst
A Data Analyst gathers, analyzes, and interprets data to help businesses make better decisions. Some of the tasks a Data Analyst may perform include gathering and cleaning data, analyzing data, and developing and deploying machine learning models. This course may be useful for someone looking to become a Data Analyst because it provides a foundation in the use of Word2Vec and LSTM for text classification, two important techniques in the field of data analysis.
Data Scientist
Data Scientist
A Data Scientist uses data to solve business problems. Some of the tasks a Data Scientist may perform include gathering and cleaning data, analyzing data, and developing and deploying machine learning models. This course may be useful for someone looking to become a Data Scientist because it provides a foundation in the use of Word2Vec and LSTM for text classification, two important techniques in the field of data science.
Marketing Manager
Marketing Manager
A Marketing Manager gathers and analyzes data to help businesses make better decisions about their marketing campaigns. Some of the tasks a Marketing Manager may perform include gathering and cleaning data, analyzing data, and developing and deploying machine learning models. This course may be useful for someone looking to become a Marketing Manager because it provides a foundation in the use of Word2Vec and LSTM for text classification, two important techniques in the field of marketing.
Product Manager
Product Manager
A Product Manager gathers and analyzes data to help businesses make better decisions about their products. Some of the tasks a Product Manager may perform include gathering and cleaning data, analyzing data, and developing and deploying machine learning models. This course may be useful for someone looking to become a Product Manager because it provides a foundation in the use of Word2Vec and LSTM for text classification, two important techniques in the field of product management.
Operations Manager
Operations Manager
An Operations Manager gathers and analyzes data to help businesses make better decisions about their operations. Some of the tasks an Operations Manager may perform include gathering and cleaning data, analyzing data, and developing and deploying machine learning models. This course may be useful for someone looking to become an Operations Manager because it provides a foundation in the use of Word2Vec and LSTM for text classification, two important techniques in the field of operations.
Consultant
Consultant
A Consultant gathers and analyzes data to help businesses make better decisions. Some of the tasks a Consultant may perform include gathering and cleaning data, analyzing data, and developing and deploying machine learning models. This course may be useful for someone looking to become a Consultant because it provides a foundation in the use of Word2Vec and LSTM for text classification, two important techniques in the field of consulting.
Customer Success Manager
Customer Success Manager
A Customer Success Manager gathers and analyzes data to help businesses make better decisions about their customer service strategies. Some of the tasks a Customer Success Manager may perform include gathering and cleaning data, analyzing data, and developing and deploying machine learning models. This course may be useful for someone looking to become a Customer Success Manager because it provides a foundation in the use of Word2Vec and LSTM for text classification, two important techniques in the field of customer success.
Teacher
Teacher
A Teacher gathers and analyzes data to help students learn. Some of the tasks a Teacher may perform include gathering and cleaning data, analyzing data, and developing and deploying machine learning models. This course may be useful for someone looking to become a Teacher because it provides a foundation in the use of Word2Vec and LSTM for text classification, two important techniques in the field of education.
Researcher
Researcher
A Researcher gathers and analyzes data to help businesses make better decisions. Some of the tasks a Researcher may perform include gathering and cleaning data, analyzing data, and developing and deploying machine learning models. This course may be useful for someone looking to become a Researcher because it provides a foundation in the use of Word2Vec and LSTM for text classification, two important techniques in the field of research.
Financial Analyst
Financial Analyst
A Financial Analyst gathers and analyzes data to help businesses make better decisions about their finances. Some of the tasks a Financial Analyst may perform include gathering and cleaning data, analyzing data, and developing and deploying machine learning models. This course may be useful for someone looking to become a Financial Analyst because it provides a foundation in the use of Word2Vec and LSTM for text classification, two important techniques in the field of finance.
Sales Manager
Sales Manager
A Sales Manager gathers and analyzes data to help businesses make better decisions about their sales strategies. Some of the tasks a Sales Manager may perform include gathering and cleaning data, analyzing data, and developing and deploying machine learning models. This course may be useful for someone looking to become a Sales Manager because it provides a foundation in the use of Word2Vec and LSTM for text classification, two important techniques in the field of sales.
Business Analyst
Business Analyst
A Business Analyst gathers and analyzes data to help businesses make better decisions. Some of the tasks a Business Analyst may perform include gathering and cleaning data, analyzing data, and developing and deploying machine learning models. This course may be useful for someone looking to become a Business Analyst because it provides a foundation in the use of Word2Vec and LSTM for text classification, two important techniques in the field of business analysis.
Software Engineer
Software Engineer
A Software Engineer designs, develops, and maintains software applications. Some of the tasks a Software Engineer may perform include gathering and analyzing requirements, designing and coding software, and testing and deploying software. This course may be useful for someone looking to become a Software Engineer because it provides a foundation in the use of Word2Vec and LSTM for text classification, two important techniques in the field of software engineering.
For more career information including salaries, visit:
OpenCourser.com/course/xyqrqc/text
Reading list
We've selected eight 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
Text Classification Using Word2Vec and LSTM on Keras.
Comprehensive textbook on speech and language processing, covering topics such as speech recognition, natural language understanding, and computational linguistics. It valuable resource for anyone looking to gain a deep understanding of the field of speech and language processing.
Provides a comprehensive overview of the statistical foundations of NLP, covering topics such as language models, information retrieval, and machine translation. It valuable resource for anyone looking to gain a deep understanding of the statistical approaches to NLP.
Provides a comprehensive overview of neural network architectures for natural language processing tasks.
Provides a comprehensive overview of machine learning techniques with Python.
Provides a comprehensive overview of NLP and its applications in the real world, covering topics such as text classification, machine translation, and information extraction. It valuable resource for anyone looking to gain a broad understanding of NLP and its potential.
Save
Provides a comprehensive overview of machine learning techniques for text, covering topics such as text classification, text generation, and text summarization. It valuable resource for anyone looking to gain a deep understanding of the field of machine learning for text.
Provides a comprehensive overview of natural language processing techniques.
Provides a comprehensive overview of deep learning techniques.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/xyqrqc/text
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Time
2 hours
Via
Coursera
Institution
Coursera Project Network
Instructor
Mohammed Murtuza Qureshi
Good to know
For those seeking an edge in understanding innovation and groundbreaking topics
Good for Students interested in learning Text Classification, Word Embeddings, LSTM Neural Networks, Deep Learning, Keras, and Tensorflow
Provides hands-on experience with Word2Vec modeling, LSTM model development, and evaluation
Suitable for learners with basic knowledge of Python and machine learning concepts
It is unclear if this course is accessible to learners outside of North America
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