Python机器学习实战:预测分析入门指南

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"Python Machine Learning(Sebastian Raschka)[PACKT]" 是一本深度指南,专为那些想要探索和掌握前沿预测分析技术的读者精心编撰。在这个数据爆炸的时代,每天产生的数据量达到了惊人的2.5万亿字节(1018),其中大部分在过去十年间涌现,对人类来说,这些海量数据往往难以理解和利用。机器学习(Machine Learning)作为关键技术,使得计算机能够处理并从看似无解的大数据中提取有价值的信息,驱动着我们日常生活中的各种技术,如搜索引擎和智能手机,尽管我们可能并未意识到。 本书由Sebastian Raschka撰写,他以其丰富的经验和深入浅出的讲解,带领读者进入Python机器学习的世界。作者旨在通过这本书,帮助读者解锁大数据背后的深层洞察,理解如何运用Python这一强大的工具来构建、训练和部署机器学习模型。无论是数据预处理、特征工程,还是常见的算法如线性回归、决策树、神经网络,乃至深度学习,都将在书中详尽探讨。 本书适合初学者和有经验的数据分析师,无论你是想提升现有技能,还是希望入门机器学习领域,都能在本书中找到关键知识点。作者不仅关注理论,还会提供实用的代码示例,确保读者能将所学应用于实践中。 然而,需要明确的是,尽管作者和Packt Publishing努力确保信息的准确性,但书中的内容并非保证无误。版权方面,未经出版商事先书面许可,任何形式的复制、存储或传输都受到限制,且本书销售时并不附带任何形式的保证,包括但不限于明示或暗示的保证。此外,关于书中提及的所有公司和产品的商标信息,虽然进行了适当的标注,但出版社并不能确保其准确性。 "Python Machine Learning(Sebastian Raschka)[PACKT]"是一本既富有理论深度又具有实战指导意义的书籍,是每个希望在这个快速发展的领域中立足的专业人士不可或缺的参考资料。通过阅读和实践,读者将能熟练掌握Python在机器学习领域的应用,并能在实际项目中发挥重要作用。
2015-11-13 上传
原pdf书签没有链接正确,本人对此进行了修正 Paperback: 454 pages Publisher: Packt Publishing - ebooks Account (September 2015) Language: English ISBN-10: 1783555130 ISBN-13: 978-1783555130 Unlock deeper insights into Machine Leaning with this vital guide to cutting-edge predictive analytics About This Book Leverage Python's most powerful open-source libraries for deep learning, data wrangling, and data visualization Learn effective strategies and best practices to improve and optimize machine learning systems and algorithms Ask and answer tough questions of your data with robust statistical models, built for a range of datasets Who This Book Is For If you want to find out how to use Python to start answering critical questions of your data, pick up Python Machine Learning whether you want to get started from scratch or want to extend your data science knowledge, this is an essential and unmissable resource. What You Will Learn Explore how to use different machine learning models to ask different questions of your data Learn how to build neural networks using Keras and Theano Find out how to write clean and elegant Python code that will optimize the strength of your algorithms Discover how to embed your machine learning model in a web application for increased accessibility Predict continuous target outcomes using regression analysis Uncover hidden patterns and structures in data with clustering Organize data using effective pre-processing techniques Get to grips with sentiment analysis to delve deeper into textual and social media data
2018-03-07 上传
Through exposure to the news and social media, you are probably aware of the fact that machine learning has become one of the most exciting technologies of our time and age. Large companies, such as Google, Facebook, Apple, Amazon, and IBM, heavily invest in machine learning research and applications for good reasons. While it may seem that machine learning has become the buzzword of our time and age, it is certainly not a fad. This exciting field opens the way to new possibilities and has become indispensable to our daily lives. This is evident in talking to the voice assistant on our smartphones, recommending the right product for our customers, preventing credit card fraud, filtering out spam from our email inboxes, detecting and diagnosing medical diseases, the list goes on and on. If you want to become a machine learning practitioner, a better problem solver, or maybe even consider a career in machine learning research, then this book is for you. However, for a novice, the theoretical concepts behind machine learning can be quite overwhelming. Many practical books have been published in recent years that will help you get started in machine learning by implementing powerful learning algorithms. Getting exposed to practical code examples and working through example applications of machine learning are a great way to dive into this field. Concrete examples help illustrate the broader concepts by putting the learned material directly into action. However, remember that with great power comes great responsibility! In addition to offering a hands-on experience with machine learning using the Python programming languages and Python-based machine learning libraries, this book introduces the mathematical concepts behind machine learning algorithms, which is essential for using machine learning successfully. Thus, this book is different from a purely practical book; it is a book that discusses the necessary details regarding machine learning con