Text Classification with Keras and Python

Text data is everywhere, but turning raw text into actionable insights requires specialized machine learning techniques. This text-based course guides you through the fundamentals of natural language processing (NLP) to build reliable text classification systems. You will start with core concepts and foundational terminology before moving on to practical model implementation. Learn how to transform words into numerical representations, establish solid baseline models, and scale up to deep learning architectures. What you'll learn: Understand foundational NLP terminology, text preprocessing, and tokenization techniques; Build baseline classification models using scikit-learn and logistic regression; Implement the bag-of-words model to represent text numerically; Design and train deep learning models for sentiment analysis using Keras; Apply modern practices like word embeddings to capture semantic meaning; Evaluate and fine-tune your text classifiers for better accuracy. The course begins with essential definitions and simple vectorization methods, then moves step-by-step through traditional machine learning classifiers, and concludes with building neural networks in Keras. This course is designed for beginners who have a basic understanding of Python programming and want to enter the field of natural language processing without any prior machine learning experience. Start reading today to master the essentials of text classification.