Text Classification with spaCy and Keras

Text data is everywhere, but turning raw prose into structured insights requires specialized tools. This course teaches you how to combine the industry-grade text preprocessing of spaCy with the deep learning power of Keras to build robust text classification models. You will start with the fundamental concepts of natural language processing, learning how to clean text, tokenize words, and prepare datasets for machine learning algorithms. Next, you will discover how to convert text into numeric representations, build neural networks, and evaluate your model's performance on real-world datasets. What you'll learn: Understand core NLP concepts like tokenization, lemmatization, and stop-word removal; Convert raw text into dense vector representations using pre-trained word embeddings; Build and compile feedforward and recurrent neural networks in Keras; Train text classification models to categorize sentiment, topics, or spam; Evaluate classification performance using precision, recall, and F1-score metrics; Apply modern best practices for structuring machine learning pipelines. The course begins with essential terminology and text-processing fundamentals before guiding you step-by-step through designing, training, and testing your own classification models. This course is designed for beginners who have a basic understanding of Python and want to enter the field of natural language processing. Start building your first deep learning text classifier today.