توضیحات
Machine Learning Engineering with Python provides a comprehensive guide to building production-ready machine learning systems. It covers the full lifecycle of ML projects, including data preprocessing, model selection, training, evaluation, deployment, and monitoring. Readers learn to implement algorithms using Python libraries such as scikit-learn, TensorFlow, and PyTorch, and gain insights into best practices for writing maintainable, scalable, and efficient ML code. Topics also include feature engineering, hyperparameter tuning, model interpretability, and MLOps. Designed for data scientists, software engineers, and AI practitioners, the book equips readers with the practical skills and knowledge to move from ML prototypes to reliable, real-world applications.
نقد و بررسیها
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