用Python设计高效机器学习系统

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"Designing Machine Learning Systems with Python"是一本由David Julian撰写的专业书籍,该书专注于指导读者如何设计高效、准确的机器学习系统。本书是2016年由Packt Publishing出版,版权受到保护,未经许可,任何复制、存储或传输行为都必须得到出版社的书面授权,除非是在进行批评性文章或评论中的引用。 本书的核心内容围绕如何使用Python这一流行的编程语言来构建机器学习项目,它强调了在实际工作中应用机器学习算法和技术的重要性,以便获得更精确的结果。作者在书中详细阐述了设计过程,涵盖了数据预处理、模型选择、特征工程、模型训练、优化以及评估等各个环节,确保读者能够掌握从头到尾构建机器学习系统的完整流程。 作为一本综合文档,它不仅教授理论知识,还提供了实践案例和实用技巧,使读者能够通过实例理解并应用所学。读者可以从中学习到如何在Python环境中有效地使用各种机器学习库,如Scikit-learn、TensorFlow和PyTorch,以及如何解决常见的机器学习问题和挑战。 此外,书中也提到了版权声明,所有信息均出于最佳实践和作者的专业判断,但不保证100%准确无误。读者在使用书中的内容时,应自行承担风险,因为该书并未提供任何形式的质保,包括明示或暗示的保证。Packt Publishing尽力确保商标信息的准确性,但并不对此承担责任。 本书适合机器学习初学者和有经验的开发人员,无论你是想提升技能,还是寻求在实际项目中应用机器学习的新思路,都能在"Designing Machine Learning Systems with Python"中找到有价值的知识和资源。随着科技的快速发展,本书的内容将持续更新,以适应不断变化的机器学习领域。首次出版于2016年,但鉴于机器学习技术的迭代,读者可以期待后续版本中的新进展和改进。
2019-04-30 上传
Practical Machine Learning with Python A Problem-Solver's Guide to Building Real-World Intelligent Systems Author: Dipanjan Sarkar, ‎ Raghav Bali, ‎ Tushar SharmaISBN-10: 1484232062Year: 2018Pages: 530Language: EnglishFile size: 19.8 MBFile format: PDFCategory: Python Book Description: Master the essential skills needed to recognize and solve complex problems with machine learning and deep learning. Using real-world examples that leverage the popular Python machine learning ecosystem, this book is your perfect companion for learning the art and science of machine learning to become a successful practitioner. The concepts, techniques, tools, frameworks, and methodologies used in this book will teach you how to think, design, build, and execute machine learning systems and projects successfully. Practical Machine Learning with Python follows a structured and comprehensive three-tiered approach packed with hands-on examples and code. Part 1 focuses on understanding machine learning concepts and tools. This includes machine learning basics with a broad overview of algorithms, techniques, concepts and applications, followed by a tour of the entire Python machine learning ecosystem. Brief guides for useful machine learning tools, libraries and frameworks are also covered. Part 2 details standard machine learning pipelines, with an emphasis on data processing analysis, feature engineering, and modeling. You will learn how to process, wrangle, summarize and visualize data in its various forms. Feature engineering and selection methodologies will be covered in detail with real-world datasets followed by model building, tuning, interpretation and deployment. Part 3 explores multiple real-world case studies spanning diverse domains and industries like retail, transportation, movies, music, marketing, computer vision and finance. For each case study, you will learn the application of various machine learning techniques and methods. The hands-on examples will help you become familiar with state-of-the-art machine learning tools and techniques and understand what algorithms are best suited for any problem. Practical Machine Learning with Python will empower you to start solving your own problems with machine learning today! What You’ll Learn Execute end-to-end machine learning projects and systems Implement hands-on examples with industry standard, open source, robust machine learning tools and frameworks Review case studies depicting applications of machine learning and deep learning on diverse domains and industries Apply a wide range of machine learning models including regression, classification, and clustering. Understand and apply the latest models and methodologies from deep learning including CNNs, RNNs, LSTMs and transfer learning. Who This Book Is For IT professionals, analysts, developers, data scientists, engineers, graduate students
2016-04-16 上传