Python machine learning

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，

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

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

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

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.

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

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 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

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

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

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

Particle Technology and Engineering: Fundamentals and Computational Methods, Second Edition provides a comprehensive reference and introduction to the topic, ranging from single particle characterization to bulk powder properties, from particle-particle interaction to particle-fluid interaction, from fundamental mechanics to advanced computational mechanics for particle and powder systems. The book begins with a theoretical framework, with the first eight chapters focusing on properties of single particles and powder systems, their characterization, particle-fluid interactions, and the mechanics of particle and powder systems. These foundational chapters provide examples and exercises to aid in understanding these concepts. The last three chapters build upon this overview to provide more advanced practical guidance focus on modeling and simulations for particle systems and powder processing, including discrete element methods, finite element methods, and machine learning - case studies

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

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。