Building Chatbots with Keras and NLP

Conversational AI is transforming how we interact with software, but building a reliable chatbot requires a solid understanding of both language structure and deep learning. This text-only course guides you through the foundational principles of Natural Language Processing (NLP) and neural networks to help you construct your own conversational agents. You will transition from understanding basic text processing to designing, training, and systematically evaluating neural chatbot models using Keras. What you'll learn: - Understand foundational NLP concepts, text preprocessing, and modern tokenization techniques. - Apply text vectorization and word embedding methods to prepare text data for neural networks. - Design neural network architectures in Keras tailored for sequence processing and intent classification. - Build a structured chatbot framework that maps user inputs to appropriate responses. - Evaluate chatbot performance using classification metrics and modern validation strategies. - Practice debugging and refining model responses through written code analysis and structured exercises. Starting with essential definitions of NLP and conversational design, the course progresses step-by-step through dataset preparation, Keras model implementation, and practical testing scenarios. This course is designed for beginners in machine learning; basic familiarity with Python is recommended, but no prior chatbot experience is required. Start reading today to build your first intelligent conversational model.