Dimitris N. Politis - Model-Free Prediction and Regression

The book is intended for those who want to learn how to use Azure Machine Learning. Perhaps you already know a bit about Machine Learning, but have never used ML Studio in Azure; or perhaps you are an absolute newbie. In either case, this book will get you up-and-running quickly.;Cover -- Copyright -- Credits -- About the Author -- Acknowledgments -- About the Reviewers -- www.PacktPub.com -- Table of Contents -- Preface -- Chapter 1: Introduction -- Introduction to predictive analytics -- Problem definition and scoping -- Data collection -- Data exploration and preparation -- Model development -- Model deployment -- Machine learning -- Kinds of machine learning problems -- Classification -- Regression -- Clustering -- Common machine learning techniques/algorithms -- Linear regression -- Logistic regression -- Decision tree-based ensemble models -- Neural networks and deep learning -- Introduction to Azure Machine Learning -- ML Studio -- Summary -- Chapter 2: ML Studio Inside Out -- Introduction to ML Studio -- Getting started with Microsoft Azure -- Microsoft account and subscription -- Creating and managing ML workspaces -- Inside ML Studio -- Experiments -- Creating and editing an experiment -- Running an experiment -- Creating and running an experiment -- do it yourself -- Workspace as a collaborative environment -- Summary -- Chapter 3: Data Exploration and Visualization -- The basic concepts -- The mean -- The median -- Standard deviation and variance -- Understanding a histogram -- The box and whiskers plot -- The outliers -- A scatter plot -- Data exploration in ML Studio -- Visualizing an automobile price dataset -- A histogram -- The box and whiskers plot -- Comparing features -- A snapshot -- Do it yourself -- Summary -- Chapter 4: Getting Data in and out of ML Studio -- Getting data in ML Studio -- Uploading data from a PC -- The Enter Data module -- The Data Reader module -- Getting data from the Web -- Getting data from Azure -- Data format conversion -- Getting data from ML Studio -- Saving dataset in a PC -- Saving results in ML Studio -- The Writer module -- Summary -- Chapter 5: Data Preparation.