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PHP and Machine Learning: A Practical Introduction for Web Developers: A Practical Guide to ML Fundamentals, Real-World Applications, and Python Integ
商品資訊
ISBN13：9798288737053
出版社：Independently published
作者：Phiquill Publishing
出版日：2025/06/19
裝訂：平裝
規格：27.9cm*21.6cm*0.9cm (高/寬/厚)
商品簡介
商品簡介
What You Will Learn in This Book
- Understand the fundamentals of machine learning (ML), including supervised, unsupervised, and reinforcement learning, and their relevance to web development.
- Set up a robust PHP development environment for ML, integrating essential tools like Composer and the PHP-ML library.
- Implement core machine learning algorithms directly in PHP using PHP-ML, covering classification (e.g., spam detection, sentiment analysis), regression (e.g., price prediction), and clustering (e.g., customer segmentation).
- Master data preprocessing and feature engineering techniques to prepare diverse datasets for ML models, including handling missing data, normalization, and text preprocessing.
- Perform advanced data acquisition and manipulation using core PHP, reading from various sources like CSV, JSON, XML, databases, and APIs, and efficiently cleaning and transforming data.
- Seamlessly integrate PHP applications with powerful Python-based ML services, leveraging popular frameworks like Scikit-learn, TensorFlow, and PyTorch.
- Implement various PHP-Python integration strategies, including executing Python scripts, consuming REST APIs, utilizing message queues for asynchronous tasks, and understanding gRPC for high-performance communication.
- Design and build hybrid PHP/Python applications through practical case studies, demonstrating how to combine PHP for frontend and data management with Python for complex ML model training and serving.
- Gain insights into advanced ML topics such as deep learning and natural language processing, understanding when and how to integrate these capabilities into your PHP projects.
- Learn best practices for deploying and maintaining ML models within PHP applications, including saving/loading models and monitoring performance.
- Develop an awareness of the limitations of PHP in ML and ethical considerations, ensuring responsible and informed application of machine learning in your projects.
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