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