Python machine learning

本書由教學與科研經驗豐富的專任教師、企業資深工程師、全國職業技能大賽一等獎獲得者共同編寫。書中依據“項目引領、任務驅動”的思路，針對數據批量處理、流式處理、機器學習等Spark典型應用情境，設計了8個教學項目，涵蓋Spark Core、Spark SQL、Spark Streaming、Structured Streaming、Spark Machine Learning等技術。其中每個項目細分為3～6個子任務，以保證技能提升的“平滑性”，契合初學者的認知規律。本書內容由淺入深，由實踐到理論，再從理論回到實踐，符合初學者的學習規律。同時，編者為了踐行立德樹人的時代擔當，將思政元素有機融入項目教學，讓讀者在完成拓展項目的同時提升個人素養。本書配套了微課視頻、PPT課件、程序代碼、數據集、教案、教學日曆、考試樣題、課程標準(大綱)等全套教學資源，以利於教師的教學。為了最大限度降低學習門檻，本書還提供了基於Linux的Spark虛擬機環境，可免去讀者配置環境的煩惱。本書可作為高等職業院校、應用型本科院校大數據相關課程的配套教材，也可作為Spark學習者的參考用書。

Timely and reliable information on natural resources, regarding their potential and limitations, is a prerequisite for sustainable development. Geospatial technologies offer immense potential in providing such information in a timely and cost-effective manner. Using orbital sensors data in conjunction with airborne and proximal sensors to generate information on soils and agricultural resources, forests, mineral resources, fossil fuel, wetlands, water resources, and marine resources, this book focuses on the advancements in technologies applicable to managing these resources. It addresses global issues like climate change and land degradation neutrality and introduces Spatial Data Infrastructure (SDI) as a mechanism for sharing geospatial data. It also provides in-depth discussion on drones, crowdsourcing, cloud computing, Internet of Things, machine learning, and their applications.Features Provides a comprehensive resource on the latest developments in geospatial technologies and the

A hands-on, code-based guide to leveraging Julia in a variety of scientific and data-driven scenarios Key Features: Augment your basic computing skills with an in-depth introduction to JuliaFocus on topic-based approaches to scientific problems and visualisationBuild on prior knowledge of programming languages such as Python, R, or C/C++Purchase of the print or Kindle book includes a free PDF eBook Book Description: Julia is a well-constructed programming language which was designed for fast execution speed by using just-in-time LLVM compilation techniques, thus eliminating the classic problem of performing analysis in one language and translating it for performance in a second.This book is a primer on Julia's approach to a wide variety of topics such as scientific computing, statistics, machine learning, simulation, graphics, and distributed computing.Starting off with a refresher on installing and running Julia on different platforms, you'll quickly get to grips with the core concept

本書共6章。第1章介紹機器學習的基本概念及其發展史、機器學習分類、常見機器學習算法及其特點；第2章搭建機器學習開發環境，主要包括Anaconda\PyCham\Python軟件的安裝及使用，以及常見機器學習庫的介紹和安裝使用方法；第3章介紹監督學習的4個經典算法：線性回歸、決策樹、k近鄰和支持向量機算法，其重點在算法的應用；第4章介紹主成分分析降維算法、K-means聚類算法；第5章介紹人工神經網絡基礎，並通過房價預測和手寫數字識別實例進行驗證；第6章介紹強化學習的基本概念，有模型學習和無模型學習，最後介紹了Q-Learning算法和Sarsa算法。

This book provides a comprehensive foundation of machine learning. To answer the questions of what to learn, how to learn, what to get from learning, and how to evaluate, as well as what is meant by learning, the book focuses on the fundamental basics of machine learning, its methodology, theory, algorithms, and evaluations, together with some philosophical thinking on comparison between machine learning and human learning for machinery intelligence.The book is organized as follows: Introduction (Chapter 1), Evaluation (Chapter 2), Supervised learning (Chapters 3, 4, and 5), Unsupervised learning (Chapter 6), Representation learning (Chapter 7), Problem decomposition (Chapter 8), Ensemble learning (Chapter 9), Deep learning (Chapter 10), Application (Chapter 11), and Challenges (Chapter 12).The book can be used as a textbook for college, undergraduate, graduate and PhD students majored in computer science, automation, electronic engineering, communication, ect. It can also be used as

Implement real-time data processing applications on the Raspberry Pi. This book uniquely helps you work with data science concepts as part of real-time applications using the Raspberry Pi as a localized cloud. You'll start with a brief introduction to data science followed by a dedicated look at the fundamental concepts of Python programming. Here you'll install the software needed for Python programming on the Pi, and then review the various data types and modules available. The next steps are to set up your Pis for gathering real-time data and incorporate the basic operations of data science related to real-time applications. You'll then combine all these new skills to work with machine learning concepts that will enable your Raspberry Pi to learn from the data it gathers. Case studies round out the book to give you an idea of the range of domains where these concepts can be applied. By the end of Data Science with the Raspberry Pi, you'll understand that many applications are now de

Many machine learning algorithms require real-valued feature vectors of data instances as inputs. By projecting data into vector spaces, representation learning techniques have achieved promising performance in many areas such as computer vision and natural language processing. There is also a need to learn representations for discrete relational data, namely networks or graphs. Network Embedding (NE) aims at learning vector representations for each node or vertex in a network to encode the topologic structure. Due to its convincing performance and efficiency, NE has been widely applied in many network applications such as node classification and link prediction.This book provides a comprehensive introduction to the basic concepts, models, and applications of network representation learning (NRL). The book starts with an introduction to the background and rising of network embeddings as a general overview for readers. Then it introduces the development of NE techniques by presenting se

Meta-Analytics: Consensus Approaches and System Patterns for Data Analysis presents an exhaustive set of patterns for data science to use on any machine learning based data analysis task. The book vir

Intelligent Data Analysis for Biomedical Applications: Challenges and Solutions presents specialized statistical, pattern recognition, machine learning, data abstraction and visualization tools for th

The award-winning author of The Music of the Primes explores the future of creativity and how machine learning will disrupt, enrich, and transform our understanding of what it means to be human.Can a well-programmed machine do anything a human can—only better? Complex algorithms are buying our groceries, picking our partners, and driving our investments. They can navigate more data than a doctor or lawyer and act with greater precision. For many years we’ve taken solace in the notion that they can’t create. But now that algorithms can learn and adapt, does the future of creativity belong to machines too?It is hard to imagine a better guide to the bewildering world of artificial intelligence than Marcus du Sautoy, a celebrated Oxford mathematician whose work on symmetry in the ninth dimension has taken him to the vertiginous edge of mathematical understanding. In The Creativity Code he considers what machine learning means for the future of creativity. Programs like Deep Dream produce d

EEG Brain Signal Classification for Epileptic Seizure Disorder Detection provides the knowledge necessary to classify EEG brain signals to detect epileptic seizures using machine learning techniques.

This book is intended to provide a systematic overview of so-called smart techniques, such as nature-inspired algorithms, machine learning and metaheuristics. Despite their ubiquitous presence and wid

The workbook / project manual is designed to help you master key chapter content and apply it in the machine shop. This resource includes review material, plus guided practice operations and projects.

This book provides comprehensive coverage of the field of outlier analysis from a computer science point of view. It integrates methods from data mining, machine learning, and statistics within the co

Sufficient dimension reduction is a rapidly developing research field that has wide applications in regression diagnostics, data visualization, machine learning, genomics, image processing, pattern re

本書作者曾在北美多家智能專業公司任高級架構師，有20年數據挖掘、機器學習方面的設計、開發、管理經驗。他結合自己多年的行業經歷，總結了自己在機器學習方面的知識和實際工程中的經驗，提供了大量一線資料。本書不僅介紹了機器學習中的常用算法，而且給出了具體實施環境和經驗總結。重點介紹了相關算法，包括：相關因子算法、聚類算法、分類算法、回歸與測試算法等。不僅列舉了詳細示例，還介紹了算法在工程實踐中的具體應用，

The proceedings of DOSFIAC 2017 consist of papers on topics such as artificial intelligence, machine learning, big data, cloud computing, IOT, smart reservoir, visualization, GIS and SCADA systems. Th

Big Data in Omics and Imaging: Association Analysis addresses the recent development of association analysis and machine learning for both population and family genomic data in sequencing era. It is u

The problems it addresses include emotion representation, annotation of music excerpts, feature extraction, and machine learning. The book chiefly focuses on content-based analysis of music files, a s

Learn how today’s businesses can transform themselves by leveraging real-time data and advanced machine learning analytics. This book provides prescriptive guidance for architects and develo

In nearly all machine learning, decisions must be made given current knowledge. Surprisingly, making what is believed to be the best decision is not always the best strategy, even when learning in a s

內容簡介:「本書提供許多絕佳的機器學習實用案例。有別於工具書或理論證明，本書著重於實際問題處理，因此具備程式設計背景及對機器學習有興趣的讀者們均可輕鬆入門。」 - Max Shron, OkCupid 如果你是平時喜歡上網蒐集各種資料的程式設計師，想尋找並學習資料分析的方法與工具，本書將會是您了解機器學習最好的起點。在Machine Learning領域中，包含各種分析問題的工具與方法，可以讓我們

The goal of structured prediction is to build machine learning models that predictrelational information that itself has structure, such as being composed of multiple interrelatedparts. These models,

Industrial Strength Empirical Modeling clearly explains the main principles of the different machine learning approaches and offers a methodology of how to integrate these techniques for successful r

This book introduces a novel paradigm for machine learning and data mining called predictive clustering, which covers a broad variety of learning tasks and offers a fresh perspective on existing techn

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

Machine learning develops intelligent computer systems that are able to generalizefrom previously seen examples. A new domain of machine learning, in which the prediction mustsatisfy the additional co

State-of-the-art algorithms and theory in a novel domain of machine learning, prediction when the output has structure. Machine learning develops intelligent computer systems that are able to genera

The minimum description length (MDL) principle is a powerful method of inductive inference, the basis of statistical modeling, pattern recognition, and machine learning. It holds that the best explan

The process of inductive inference -- to infer general laws and principles from particular instances -- is the basis of statistical modeling, pattern recognition, and machine learning. The Minimum Des

Data mining, or knowledge discovery, has become an indispensable technology for businesses and researchers in many fields. Drawing on work in such areas as statistics, machine learning, pattern recog

This book covers the techniques of data mining, knowledge discovery, genetic algorithms, neural networks, bootstrapping, machine learning, and Monte Carlo simulation. Computational finance, an excit

Although Inductive Logic Programming (ILP) is generally thought of as a research area at the intersection of machine learning and computational logic, Bergadano and Gunetti propose that most of the re

A concise, rigorous, yet accessible, account of the fundamentals of constrained optimization theory. Many problems arising in diverse fields such as machine learning, medicine, chemical engineering, s