Machine Learning and Language AI introduces students to the core concepts and practical workflows behind modern machine learning and natural language processing. Learners explore supervised and unsupervised models, model evaluation, optimization, recommendation systems, text preprocessing, embeddings, sentiment analysis, topic modeling, and applied NLP pipelines.
Designed for college courses and workforce programs, the text emphasizes clarity, real-world application, and hands-on learning with guided exercises and real datasets. Students gain a practical understanding of how ML and NLP models are developed, evaluated, and applied in business and industry contexts.
Topics include ML fundamentals, evaluation metrics, optimization, text cleaning, tokenization, embeddings, classification, topic modeling, recommendation systems, and applied AI workflows. Instructor resources are available from the publisher.
