If you are a solutions architect, technical consultant, administrator, or any other virtualization enthusiast who needs to use Microsoft System Center Virtual Machine Manager in a real-world environment, then this is the book for you.

If you are new to Configuration Manager or have experience with it, and are interested in identifying, diagnosing and resolving the System Center Configuration Manager administration issues then this book is for you.

If you are new to Configuration Manager or have experience with it, and are interested in identifying, diagnosing and resolving the System Center Configuration Manager administration issues then this book is for you.

This book is a step-by-step tutorial that guides you through the key steps in implementing best solutions for high availability and performance tuning. It is split into two distinct approaches: client and site side HA and optimization.Microsoft SCCM High Availability and Performance Tuning is for IT professionals and consultants working with Configuration Manager who wish to learn the skills to deploy a redundant and scalable solution.

If you are an experienced Configuration Manager administrator looking to advance your career or get more from your current environment, then this book is ideal for you. Prior experience of deploying and managing a Configuration Manager site would be helpful in following the examples throughout this book.

If you are an administrator of a VMware vSphere infrastructure and want to simplify storage delivery by integrating storage into vSphere, this book is for you. No extensive storage background is needed as VMware Virtual SAN integrates into the existing vSphere solutions with which you are already familiar.

This book is for analysts, statisticians, and data scientists with knowledge of fundamentals of machine learning and statistics, who need help in dealing with challenging scenarios faced every day of working in the field of machine learning and improving system performance and accuracy. It is assumed that as a reader you have a good understanding of mathematics. Working knowledge of R is expected.

Ensemble modeling is an approach used to improve the performance of machine learning models. It combines two or more similar or dissimilar machine learning algorithms to deliver superior intellectual powers. This book will help you to implement popular machine learning algorithms to cover different paradigms of ensemble machine learning such as boosting, bagging, and stacking.The Ensemble Machine Learning Cookbook will start by getting you acquainted with the basics of ensemble techniques and exploratory data analysis. You'll then learn to implement tasks related to statistical and machine learning algorithms to understand the ensemble of multiple heterogeneous algorithms. It will also ensure that you don't miss out on key topics, such as like resampling methods. As you progress, you’ll get a better understanding of bagging, boosting, stacking, and working with the Random Forest algorithm using real-world examples. The book will highlight how these ensemble methods use multiple models to improve machine learning results, as compared to a single model. In the concluding chapters, you'll delve into advanced ensemble models using neural networks, natural language processing, and more. You’ll also be able to implement models such as fraud detection, text categorization, and sentiment analysis.By the end of this book, you'll be able to harness ensemble techniques and the working mechanisms of machine learning algorithms to build intelligent models using individual recipes.

Machine learning is becoming increasingly pervasive in the modern data-driven world. It is used extensively across many fields such as search engines, robotics, self-driving cars, and more. With this book, you will learn how to perform various machine learning tasks in different environments. We'll start by exploring a range of real-life scenarios where machine learning can be ...(展开全部)