Sentiment Analysis with RNNs in Keras
By External Provider
About 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.
What you will learn
- Preprocess and tokenize text for sentiment analysis.
- Build and train LSTM models using Keras.
- Evaluate and visualize model performance in Colab.