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Sentiment Analysis with RNNs in Keras

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Sentiment Analysis with RNNs in Keras

This course is part of Keras Deep Learning Projects with TensorFlow Specialization

Instructor: EDUCBA

Included with

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1 module

Gain insight into a topic and learn the fundamentals.

3 hours to complete

Flexible schedule

Learn at your own pace

1 module

Gain insight into a topic and learn the fundamentals.

3 hours to complete

Flexible schedule

Learn at your own pace

What you'll learn

Preprocess and tokenize text for sentiment analysis.
Build and train LSTM models using Keras.
Evaluate and visualize model performance in Colab.

Skills you'll gain

Tools you'll learn

Keras (Neural Network Library)

Details to know

Shareable certificate

Add to your LinkedIn profile

Assessments

4 assignments

Taught in English

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Build your subject-matter expertise

This course is part of the Keras Deep Learning Projects with TensorFlow Specialization

When you enroll in this course, you'll also be enrolled in this Specialization.

Learn new concepts from industry experts
Gain a foundational understanding of a subject or tool
Develop job-relevant skills with hands-on projects
Earn a shareable career certificate

There is 1 module in this course

By the end of this course, learners will be able to explain sentiment analysis concepts, apply preprocessing techniques, and construct, train, and evaluate LSTM models using Keras in Google Colab.

This project-based course guides learners step by step through the complete workflow of sentiment analysis using the IMDB dataset. Starting with setting up the Colab environment and downloading data, learners will prepare text sequences using tokenization and padding. The course then introduces the fundamentals of Long Short-Term Memory (LSTM) networks before progressing to building, training, and evaluating both simple and complex RNN models. Learners will also practice plotting results and predicting movie review sentiments, strengthening their applied deep learning skills. What makes this course unique is its hands-on approach: every concept is directly tied to practical implementation in Python, ensuring learners not only understand the theory but also gain real-world coding experience. By completing this course, learners will be equipped with the ability to analyze text data, optimize RNN models, and apply deep learning for NLP tasks with confidence.

Module details

This module introduces learners to sentiment analysis using Recurrent Neural Networks (RNNs) in Keras. Learners will explore data preprocessing, sequence handling, and model design with Long Short-Term Memory (LSTM) networks. The module covers building, training, and evaluating both simple and complex LSTM models, empowering learners to classify IMDB movie reviews with high accuracy.