We may earn an affiliate commission when you visit our partners.
Course image
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 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.
Enroll now

Two deals to help you save

We found two deals and offers that may be relevant to this course.
Save money when you learn. All coupon codes, vouchers, and discounts are applied automatically unless otherwise noted.

Good to know

Know what's good
, what to watch for
, and 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

Save this course

Save Text Classification Using Word2Vec and LSTM on Keras to your list so you can find it easily later:
Save

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
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
Get a strong foundation in the fundamentals of natural language processing and how to use the Natural Language Toolkit (NLTK) library in Python.
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
Get a strong foundation in the fundamentals of deep learning and how to use the Keras library in Python.
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
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
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
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
Apply your knowledge of text classification to a real-world problem and gain experience in web development.
Browse courses on Text Classification
Show steps
  • Design the web application.
  • Implement the web application.
  • Deploy the web application.
Contribute to an open-source text classification project
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.

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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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.

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 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.
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.

Share

Help others find this course page by sharing it with your friends and followers:

Similar courses

Here are nine courses similar to Text Classification Using Word2Vec and LSTM on Keras.
Natural Language Processing for Stocks News Analysis
Most relevant
Transfer Learning for Food Classification
Most relevant
Emotion AI: Facial Key-points Detection
Most relevant
Anomaly Detection in Time Series Data with Keras
Most relevant
Fake News Detection with Machine Learning
Most relevant
Facial Expression Classification Using Residual Neural...
PyTorch Ultimate 2024: From Basics to Cutting-Edge
Building Recommendation System Using MXNET on AWS...
Natural Language Processing with Sequence Models
Our mission

OpenCourser helps millions of learners each year. People visit us to learn workspace skills, ace their exams, and nurture their curiosity.

Our extensive catalog contains over 50,000 courses and twice as many books. Browse by search, by topic, or even by career interests. We'll match you to the right resources quickly.

Find this site helpful? Tell a friend about us.

Affiliate disclosure

We're supported by our community of learners. When you purchase or subscribe to courses and programs or purchase books, we may earn a commission from our partners.

Your purchases help us maintain our catalog and keep our servers humming without ads.

Thank you for supporting OpenCourser.

© 2016 - 2024 OpenCourser