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Text Features and Neural Classification

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Text Features and Neural Classification

Instructor: Matt Bushby

Included with Learn more

3 modules

Gain insight into a topic and learn the fundamentals.

Intermediate level

Some related experience required

4 hours to complete

Flexible schedule

Learn at your own pace

3 modules

Gain insight into a topic and learn the fundamentals.

Intermediate level

Some related experience required

4 hours to complete

Flexible schedule

Learn at your own pace

What you'll learn

Extract features that represent text for classification.
Interpret accuracy, precision, and recall when evaluating classification results.
Explain why class imbalance affects the interpretation of model performance.
Describe the structure and learning process of feedforward neural networks used for text classification.

Skills you'll gain

Tools you'll learn

Keras (Neural Network Library)

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There are 3 modules in this course

Text classification depends on how language is represented and how a model learns from those representations. This course examines feature extraction for text and the evaluation of classification systems, including cases where some categories occur much more frequently than others.

You will interpret accuracy, precision, and recall and consider why a single measure may give an incomplete picture of performance. You will then explore neural approaches to text classification, including deep learning concepts, the structure of feedforward neural networks, and how neural networks learn. By the end of the course, you will be able to extract features for classification, interpret evaluation results on imbalanced data, and explain how a feedforward neural network supports text classification.

In this topic, you will continue to learn about text classification, the general process for text classification and how to evaluate a text classification system.

What's included

1 assignment4 plugins

In this topic, you will learn about neural networks for text classification. Neural text classification is a powerful machine learning technique that uses deep neural networks to automatically categorise text into predefined categories or classes. With the rapid growth of text data in various domains, such as social media, customer reviews, and news articles, the need for automated text classification has become increasingly important.

What's included

1 assignment6 plugins

Learners act as an analyst for an online community that wants to detect abusive forum posts, which are rare. They compare three trial classifiers on unbalanced data, turn a small document collection into vectors and tf.idf weights, and specify a feedforward neural network for the task. The result is a short design and evaluation memo for a moderation lead.

What's included

3 readings1 assignment

Instructor

Matt Bushby

Macquarie University

37 Courses34,324 learners

Offered by

Macquarie University

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Frequently asked questions

You should be familiar with text classification, the steps involved in building a classifier, and basic evaluation concepts. If these topics are new to you, start with Introduction to Natural Language Processing . You can begin here if you already understand those foundations.

You’ll explore how to represent a document collection using vectors, a matrix, term frequency, and TF-IDF weights. In the mini project, you’ll apply these representations to a small document collection as part of designing a text classifier.

You’ll compare classifiers using precision, recall, F1, and accuracy, and explain which measures to trust on imbalanced data. The mini project uses rare abusive forum posts to help you assess why a high accuracy score may give an incomplete picture of performance.

You’ll examine feedforward neural networks, their structure, and how they learn. For the mini project, you’ll specify layers, activation functions, an output layer, and a loss function for a forum moderation task. You’ll bring your design choices and evaluation findings together in a memo for a moderation lead.

To access course materials, assignments, and earn a Certificate, you'll need to purchase the Certificate experience when you enroll in a course. Eligible learners may also have the option to start with a Free Trial. Some courses may also offer a Full Course, No Certificate option. This lets you access course materials, submit required assessments, and receive a final grade, but you won't be able to earn or purchase a Certificate.