The text outlines the use of the bag-of-words model, a basic natural language processing (NLP) technique, to classify SMS messages as either ham or spam. It provides a hands-on approach using a dataset of over 5500 English messages, focusing on converting text data into a numeric format that can be processed by algorithms. The process involves text cleaning steps like removing stopwords, punctuation, and numbers, converting text to lowercase, and applying stemming and lemmatization for better comprehension. Visualization tools like word clouds are used to understand the data, and the CountVectorizer from scikit-learn is employed to transform the pre-processed data into a machine-readable form. The model's simplicity is highlighted, as it doesn't consider the order or relationships between words, which can limit its effectiveness. Despite these limitations, a Naive Bayes classifier achieves around 80% accuracy, demonstrating the model's ability to distinguish between ham and spam messages. The text suggests potential improvements and future explorations using more advanced machine learning and deep learning techniques for spam filtering.