Geo.Engineerings

Discover best practices for data analysis and software development in R and start on the path to becoming a fully-fledged data scientist. This book teaches you techniques for both data manipulation and visualization and shows you the best way for developing new software packages for R. Beginning Data Science in R details how data science is a combination of statistics, computational science, and machine learning. You’ll see how to efficiently structure and mine data to extract useful patterns and build mathematical models. This requires computational methods and programming, and R is an ideal programming language for this. This book is based on a number of lecture notes for classes the author has taught on data science and statistical programming using the R programming language. Modern data analysis requires computational skills and usually a minimum of programming. What You Will Learn: Perform data science and analytics using statistics and the R programming language + Visualize and explore data, including working with large data sets found in big data + Build an R package + Test and check your code + Practice version control + Profile and optimize your code.

Learn how to use TensorFlow 2.0 to build machine learning and deep learning models with complete examples: The book begins with introducing TensorFlow 2.0 framework and the major changes from its last release. Next, it focuses on building Supervised Machine Learning models using TensorFlow 2.0. It also demonstrates how to build models using customer estimators. Further, it explains how to use TensorFlow 2.0 API to build machine learning and deep learning models for image classification using the standard as well as custom parameters. You'll review sequence predictions, saving, serving, deploying, and standardized datasets, and then deploy these models to production. All the code presented in the book will be available in the form of executable scripts at Github which allows you to try out the examples and extend them in interesting ways.

What You'll Learn: Review the new features of TensorFlow 2.0 + Use TensorFlow 2.0 to build machine learning and deep learning models + Perform sequence predictions using TensorFlow 2.0 + Deploy TensorFlow 2.0 models with practical examples