Python machine learning

Recent startling successes in machine intelligence using a technique called ‘deep learning’ seem to blur the line between human and machine as never before. Are computers on the cusp of be

This book shows machine learning enthusiasts and practitioners how to get the best of both worlds by deriving Fisher kernels from deep learning models. In addition, the book shares insight on how to s

Malware Data Science explains how to identify, analyze, and classify large-scale malware using machine learning and data visualization.Security has become a "big data" problem. The growth rate of malw

This book presents conjectural advances in big data analysis, machine learning and computational intelligence, as well as their potential applications in scientific computing. It discusses major issue

This Level 1 guided reader series explores how road rollers are used on construction sites. Students will develop word recognition and reading skills while learning about exciting construction machine

近年來，機器學習領域受到越來越多的關注，相關的機器學習演算法開始成為熱點。 本書專門介紹了有關機器學習的內容，全書共分3部分：第1部分是資料科學和Microsoft Azure Machine Learning導論，介紹了資料科學和Microsoft Azure Machine Learning的基本知識以及需要用到的語言的基本知識；第二部分是統計學和機器學習演算法，系統地講解了統計學和機器學習的

本書探索了一系列廣泛應用的機器學習技術，用實際例子揭示了常見數據中隱藏的處理技巧和竅門。本書還探討了機器學習的前沿進展，提供了一些關於深度學習和強化學習的範例和指導原則，完美演繹了實操過程及算法原理。本書的目標讀者為那些想瞭解機器學習實踐通過機器學習技術解決現實應用的數據科學家。

Machine Learning Techniques for Space Weather provides a thorough and accessible presentation of machine learning techniques that can be employed by space weather professionals. Additionally, it prese

本書講解的是在R語言平臺上使用大數據技術構建可擴展機器學習模型的新技術成果。它全面展示了如何採用機器學習算法在原始數據的基礎上構建機器學習模型。本書還能讓那些希望利用ApacheHadoop、Hive、Pig和Spark來實現可擴展機器學習模型的讀者從中受益。

Machine learning and computerized data analysis is incredibly powerful, but humans still excel at "getting a sense of things"—the ability to recognize ill-defined or novel patterns or anomalies.

本書採用理論與實踐相結合的方式，在簡明扼要地闡明機器學習原理的基礎上，通過大量實例介紹了不同場景下機器學習算法在scikit-learn中的實現及應用。書中還有大量的代碼示例及圖例，便於讀者理解和學習並實際上手操作。另一方面，書中還有很多的延伸閱讀指導，方便讀者系統性地瞭解機器學習領域的現有技術及其發展狀態。

本書對機器學習的關鍵知識點進行了全面講解，幫助讀者順利完成從理論到實踐的過渡。書中首先介紹用於描述機器學習算法的統計與概率的知識，接著詳細分析機器學習技術的兩類主要方法――生成方法和判別方法，後深入研究了如何使機器學習算法在實際應用中發揮更大的作用。本書提供程序源代碼，便於讀者進行數據分析實踐。本書適合高等院校計算機、統計等專業的研究生和高年級本科生閱讀，同時也適合相關領域的技術人員參考。

A concise introduction to the emerging field of data science, explaining its evolution, relation to machine learning, current uses, data infrastructure issues, and ethical challenges.The goal of data

Become a machine learning pro! Google TensorFlow has become the darling of financial firms and research organizations, but the technology can be intimidating and the learning curve is steep. Luc

本書以新的Spark2.0爲技術基礎，重點講解了如何構建機器學習系統以及如何實現機器學習流程的標準化，這兩點都是目前同類書中沒有的。第1～7章從概念、架構、算法等角度介紹了機器學習的基本概念；第8～12章以實例爲主，詳細講解了機器學習流程標準化涉及的關鍵技術；第13章主要以在綫數據或流式數據爲主介紹了流式計算框架SparkStreaming；第14章重點講解了深度學習的框架TensorFlowOn

The book ‘Data Intensive Computing Applications for Big Data’ discusses the technical concepts of big data, data intensive computing through machine learning, soft computing and parallel computing par

Predictive Analytics has revolutionised marketing practice. It involves using many techniques from data mining, statistics, modelling, machine learning and artificial intelligence, to analyse current

This book is published open access under a CC BY 4.0 license.It covers aspects of unsupervised machine learning used for knowledge discovery in data science and introduces a data-driven approach to cl

This book provides over 1,000 review questions and answers for all types of mechanical engineering exams. It covers all the aspects of mechanical engineering topics including thermodynamics, machine d

Get started with Julia for engineering and numerical computing, especially data science, machine learning, and scientific computing applications. This book explains how Julia provides the functionalit

Machine Learning: A Constraint-Based Approach provides readers with a refreshing look at the basic models and algorithms of machine learning, with an emphasis on current topics of interest that includ

Machine learning algorithms and artificial intelligence influence many aspects of life today. This report identifies some of their shortcomings and associated policy risks and examines some approaches

Learn the fundamental aspects of the business statistics, data mining, and machine learning techniques required to understand the huge amount of data generated by your organization. This book explains

Master the basics of print interpretation! PRINT READING FOR MACHINISTS, Sixth Edition, is an ideal resource for machine trades students and apprentices alike who want to gain the knowledge and skills

There’s growing interest in learning how to analyze streaming data in large-scale systems such as web traffic, financial transactions, machine logs, industrial sensors, and many others. But analyzing

Till Albert presents a machine learning based approach to harnessing information contained in big data from different media sources such as patents, scientific publications, or the internet. He shows

If you’re a data scientist ready to tackle statistical and machine learning techniques across large data sets, this practical guide provides a solid introduction to the world of clustered computing an

Unleash the power of computer vision with Python using OpenCVAbout This BookCreate impressive applications with OpenCV and PythonFamiliarize yourself with advanced machine learning conceptsHarness the

Practical Graph Analytics with Apache Giraph helps you build data mining and machine learning applications using the Apache Foundation’s Giraph framework for graph processing. This is the same framewo

This book proposes a novel approach for time-series prediction using machine learning techniques with automatic feature generation. Application of machine learning techniques to predict time-series co

Linguistic Issues in Language Technology (LiLT) is an open-access journal that focuses on the relationships between linguistic insights and language technology. In conjunction with machine learning an

Idiot's Guides: Beginning Programming takes the fear out of learning programming by teaching readers the basics with Python, an open-source (free) environment which is considered one of the easiest la

This SpringerBrief reveals the latest techniques in computer vision and machine learning on robots that are designed as accurate and efficient military snipers. Militaries around the world are investi

Originating from the authors' own graduate course at the University of North Carolina, this material has been thoroughly tried and tested over many years, making the book perfect for a two-term course or for self-study. It provides a concise introduction that covers all of the measure theory and probability most useful for statisticians, including Lebesgue integration, limit theorems in probability, martingales, and some theory of stochastic processes. Readers can test their understanding of the material through the 300 exercises provided. The book is especially useful for graduate students in statistics and related fields of application (biostatistics, econometrics, finance, meteorology, machine learning, and so on) who want to shore up their mathematical foundation. The authors establish common ground for students of varied interests which will serve as a firm 'take-off point' for them as they specialize in areas that exploit mathematical machinery.

Boosting is an approach to machine learning based on the idea of creating a highlyaccurate predictor by combining many weak and inaccurate "rules of thumb." A remarkablyrich theory has evolved around

C++ is a great place to learn programming. Learn the syntax of C++ and learning languages like Java, JavaScript, PHP, Python and many others are much easier.The way to learn programming is by doing. T

What is knowledge and how is it represented? This book focuses on the idea of formalising knowledge as relations, interpreting knowledge represented in databases or logic programs as relational data and discovering new knowledge by identifying hidden and defining new relations. After a brief introduction to representational issues, the author develops a relational language for abstract machine learning problems. He then uses this language to discuss traditional methods such as clustering and decision tree induction, before moving onto two previously underestimated topics that are just coming to the fore: rough set data analysis and inductive logic programming. Its clear and precise presentation is ideal for undergraduate computer science students. The book will also interest those who study artificial intelligence or machine learning at the graduate level. Exercises are provided and each concept is introduced using the same example domain, making it easier to compare the individual pro

If you would like to teach a special child how to sew, this book presents a series of skills in a learning sequence that takes the new seamstress from the first use of a sewing machine through making

SummaryMahout in Action is a hands-on introduction to machine learning with Apache Mahout. Following real-world examples, the book presents practical use cases and then illustrates how Mahout can be a

To help you navigate the large number of new data tools available, this guide describes 60 of the most recent innovations, from NoSQL databases and MapReduce approaches to machine learning and visuali