Text and illustrations introduce the big machines such as bulldozers, cranes, cement mixers, etc. which make man's life easier.

This photo-illustrated book for early readers tells about tractor-trailers, or semis, and how these large trucks deliver goods we use every day.

Ricky Rice is a middling hustler with a lingering junk habit, a bum knee, and a haunted mind. A survivor of a suicide cult, he scrapes by as a porter at a bus depot in Utica, New York, until one day a mysterious letter arrives, summoning him to enlist in a band of paranormal investigators comprised of former addicts and petty criminals, all of whom had at some point in their wasted lives heard what may have been the voice of God. Infused with the wonder of a disquieting dream and laced with Victor LaValle’s fiendish comic sensibility, Big Machine is a mind-rattling mystery about doubt, faith, and the monsters we carry within us.

Open out the giant fold-out pages to find out about some of the world's biggest, strongest and tallest machines. Full of the world’s biggest machines found on building sites, farms, airports and dockyards including one of the biggest machines ever, the bucket-wheel excavator used in mining. For the biggest of machines, the book includes two giant foldout pages. This attractive picture book format replaces the original board book format, ISBN 9781409507314.

This book provides illustrations and fold-out pictures of machines that are used in a city.

‘With the call of ‘Hey, you guys! Let’s get to work,’ women and men shoulder drills and picks, board cranes and cement mixers, and set their equipment bulldozing and steamrolling across vibrant page spreads. Barton generates the excitement of road and building construction for young sidewalk engineers.’ —BL. 1988 Fanfare Honor List (The Horn Book) Notable 1987 Children's Trade Books in Social Studies (NCSS/CBC) Outstanding Science Trade Books for Children 1987 (NSTA/CBC) 1987 Children's Books (NY Public Library)

Introduces professionals and scientists to statistics and machine learning using the programming language R Written by and for practitioners, this book provides an overall introduction to R, focusing on tools and methods commonly used in data science, and placing emphasis on practice and business use. It covers a wide range of topics in a single volume, including big data, databases, statistical machine learning, data wrangling, data visualization, and the reporting of results. The topics covered are all important for someone with a science/math background that is looking to quickly learn several practical technologies to enter or transition to the growing field of data science. The Big R-Book for Professionals: From Data Science to Learning Machines and Reporting with R includes nine parts, starting with an introduction to the subject and followed by an overview of R and elements of statistics. The third part revolves around data, while the fourth focuses on data wrangling. Part 5 teaches readers about exploring data. In Part 6 we learn to build models, Part 7 introduces the reader to the reality in companies, Part 8 covers reports and interactive applications and finally Part 9 introduces the reader to big data and performance computing. It also includes some helpful appendices. Provides a practical guide for non-experts with a focus on business users Contains a unique combination of topics including an introduction to R, machine learning, mathematical models, data wrangling, and reporting Uses a practical tone and integrates multiple topics in a coherent framework Demystifies the hype around machine learning and AI by enabling readers to understand the provided models and program them in R Shows readers how to visualize results in static and interactive reports Supplementary materials includes PDF slides based on the book’s content, as well as all the extracted R-code and is available to everyone on a Wiley Book Companion Site The Big R-Book is an excellent guide for science technology, engineering, or mathematics students who wish to make a successful transition from the academic world to the professional. It will also appeal to all young data scientists, quantitative analysts, and analytics professionals, as well as those who make mathematical models.