Sentiment Analysis with RNNs and Keras

Text data is everywhere, but extracting meaning from millions of reviews, social media posts, and customer feedback requires specialized machine learning techniques. Understanding how to build sentiment analysis models allows you to automatically classify text as positive, negative, or neutral. This text-only course guides you through the entire pipeline of Natural Language Processing (NLP) using the Keras framework. You will transition from understanding basic text representation to designing, training, and evaluating recurrent neural networks (RNNs) that can comprehend sequential text data. By reading clear explanations and studying structured code examples, you will gain the practical skills needed to work with text datasets. What you'll learn: - Understand foundational NLP concepts, text preprocessing, and tokenization. - Map words to dense vectors using Keras embedding layers. - Build recurrent neural network architectures for sequential text classification. - Implement Long Short-Term Memory (LSTM) networks to capture long-term text dependencies. - Evaluate model performance using accuracy, precision, recall, and loss metrics. - Explore how modern sentiment analysis paradigms compare to classic recurrent structures. The course begins with key terminology, basic text preprocessing, and foundational definitions before moving into practical modeling. You will then explore RNN and LSTM architectures, learning how to structure layers and compile models using Keras. This course is designed for beginners in deep learning and NLP. A basic familiarity with Python programming is recommended, but no prior experience with neural networks or Keras is required. Start reading today to build your first deep learning models for text classification.