Sentiment Analysis with RNNs in Keras
About this course
Build practical sentiment analysis skills using Recurrent Neural Networks (RNNs), Long Short-Term Memory (LSTM) networks, Keras, and Python. Designed for learners who want hands-on experience with deep learning for Natural Language Processing (NLP), this project-based course guides you through classifying IMDB movie reviews by sentiment in Google Colab. You’ll begin by exploring sentiment analysis fundamentals, setting up the Colab environment, and downloading the IMDB dataset. You’ll then prepare text sequences for RNN training through tokenization and padding. As you progress, you’ll learn the foundations of LSTM networks and construct, train, and evaluate both simple and complex LSTM models. You’ll also plot model results, predict movie review sentiments, and optimize RNN models to improve classification accuracy. What makes this course distinctive is its step-by-step, implementation-focused approach: each concept is connected directly to practical Python coding. By the end, you’ll be able to preprocess text data, design and assess LSTM-based sentiment analysis models, interpret results, and apply deep learning techniques to NLP tasks. Enroll to build an end-to-end sentiment analysis workflow and strengthen your applied RNN and Keras skills.
75/100
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- What the provider tells you
- 39/45
- Who stands behind it
- 20/35
- How complete the listing is
- 16/20
Scores how much the provider publishes and who stands behind it — not how well it is taught.
What you'll learn
- explain sentiment analysis concepts
- apply preprocessing techniques
- construct LSTM models using Keras
- train and evaluate RNN models
- predict movie review sentiments
Course objectives
- guide learners through the complete workflow of sentiment analysis
- strengthen applied deep learning skills
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