This book introduces Raspberry Pi, using real world applications in computer vision, machine learning, and deep learning. It provides a detailed, step-by-step, approach to application development for
Carry out a variety of advanced statistical analyses including generalized additive models, mixed effects models, multiple imputation, machine learning, and missing data techniques using R. Each chapt
Examine business problems and use a practical analytical approach to solve them by implementing predictive models and machine learning techniques using SAS and the R analytical language. This bo
Gain a broad foundation of advanced data analytics concepts and discover the recent revolution in databases such as Neo4j, Elasticsearch, and MongoDB. This book discusses how to implement ETL techniqu
Machine learning can be a difficult subject if you’re not familiar with the basics. With this book, you'll get a solid foundation of introductory principles used in machine learning with the sta
Understand deep learning, the nuances of its different models, and where these models can be applied.The abundance of data and demand for superior products/services have driven the development of adva
Tackle the real-world complexities of modern machine learning with innovative, cutting-edge, techniquesAbout This BookFully-coded working examples using a wide range of machine learning libraries and
A practitioner’s tools have a direct impact on the success of his or her work. This book will provide the data scientist with the tools and techniques required to excel with statistical learning metho
This book introduces the statistical software R to the image processing community in an intuitive and practical manner. R brings interesting statistical and graphical tools which are important and nec
Field offers social science undergraduate and graduate students a textbook on statistics using R that aims to make the learning process fun and the math accessible. It draws the theory and examples fr
In machine learning applications, practitioners must take into account the cost associated with the algorithm. These costs include: Cost of acquiring training dataCost of data annotation/labeling and
