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Predictive analytics with Microsoft Azure machine learning : build and deploy actionable solutions in minutes /
Predictive Analytics with Microsoft Azure Machine Learning, Second Edition is a practical tutorial introduction to the field of data science and machine learning, with a focus on building and deploying predictive models. The book provides a thorough overview of the Microsoft Azure Machine Learning s...
| Clasificación: | Libro Electrónico |
|---|---|
| Autores principales: | , , |
| Formato: | Electrónico eBook |
| Idioma: | Inglés |
| Publicado: |
[Berkley, CA] :
Apress,
2015.
|
| Edición: | Second edition. |
| Temas: | |
| Acceso en línea: | Texto completo (Requiere registro previo con correo institucional) |
Tabla de Contenidos:
- Machine generated contents note: pt. I Introducing Data Science and Microsoft Azure Machine Learning
- ch. 1 Introduction to Data Science
- What is Data Science?
- Analytics Spectrum
- Descriptive Analysis
- Diagnostic Analysis
- Predictive Analysis
- Prescriptive Analysis
- Why Does It Matter and Why Now?
- Data as a Competitive Asset
- Increased Customer Demand
- Increased Awareness of Data Mining Technologies
- Access to More Data
- Faster and Cheaper Processing Power
- Data Science Process
- Common Data Science Techniques
- Classification Algorithms
- Clustering Algorithms
- Regression Algorithms
- Simulation
- Content Analysis
- Recommendation Engines
- Cutting Edge of Data Science
- Rise of Ensemble Models
- Summary
- Bibliography
- ch. 2 Introducing Microsoft Azure Machine Learning
- Hello, Machine Learning Studio!
- Components of an Experiment
- Introducing the Gallery
- Five Easy Steps to Creating a Training Experiment
- Step 1 Getting the Data
- Step 2 Preprocessing the Data
- Step 3 Defining the Features
- Step 4 Choosing and Applying Machine Learning Algorithms
- Step 5 Predicting Over New Data
- Deploying Your Model in Production
- Creating a Predictive Experiment
- Publishing Your Experiment as a Web Service
- Accessing the Azure Machine Learning Web Service
- Summary
- ch. 3 Data Preparation
- Data Cleaning and Processing
- Getting to Know Your Data
- Missing and Null Values
- Handling Duplicate Records
- Identifying and Removing Outliers
- Feature Normalization
- Dealing with Class Imbalance
- Feature Selection
- Feature Engineering
- Binning Data
- Curse of Dimensionality
- Summary
- ch. 4 Integration with R
- R in a Nutshell
- Building and Deploying Your First R Script
- Using R for Data Preprocessing
- Using a Script Bundle (ZIP)
- Building and Deploying a Decision Tree Using R
- Summary
- ch. 5 Integration with Python
- Overview
- Python Jumpstart
- Using Python in Azure ML Experiments
- Using Python for Data Preprocessing
- Combining Data using Python
- Handling Missing Data Using Python
- Feature Selection Using Python
- Running Python Code in an Azure ML Experiment
- Summary
- pt. II Statistical and Machine Learning Algorithms
- ch. 6 Introduction to Statistical and Machine Learning Algorithms
- Regression Algorithms
- Linear Regression
- Neural Networks
- Decision Trees
- Boosted Decision Trees
- Classification Algorithms
- Support Vector Machines
- Bayes Point Machines
- Clustering Algorithms
- Summary
- pt. III Practical Applications
- ch. 7 Building Customer Propensity Models
- Business Problem
- Data Acquisition and Preparation
- Data Analysis
- Training the Model
- Model Testing and Validation
- Model Performance
- Prioritizing Evaluation Metrics
- Summary
- ch. 8 Visualizing Your Models with Power BI
- Overview
- Introducing Power BI
- Three Approaches for Visualizing with Power BI
- Scoring Your Data in Azure Machine Learning and Visualizing in Excel
- Scoring and Visualizing Your Data in Excel
- Scoring Your Data in Azure Machine Learning and Visualizing in powerbi.com
- Loading Data
- Building Your Dashboard
- Summary
- ch. 9 Building Churn Models
- Churn Models in a Nutshell
- Building and Deploying a Customer Churn Model
- Preparing and Understanding Data
- Data Preprocessing and Feature Selection
- Classification Model for Predicting Churn
- Evaluating the Performance of the Customer Churn Models
- Summary
- ch. 10 Customer Segmentation Models
- Customer Segmentation Models in a Nutshell
- Building and Deploying Your First K-Means Clustering Model
- Feature Hashing
- Identifying the Right Features
- Properties of K-Means Clustering
- Customer Segmentation of Wholesale Customers
- Loading the Data from the UCI Machine Learning Repository
- Using K-Means Clustering for Wholesale Customer Segmentation
- Cluster Assignment for New Data
- Summary
- ch. 11 Building Predictive Maintenance Models
- Overview
- Predictive Maintenance Scenarios
- Business Problem
- Data Acquisition and Preparation
- Dataset
- Data Loading
- Data Analysis
- Training the Model
- Model Testing and Validation
- Model Performance
- Techniques for Improving the Model
- Upsampling and Downsampling
- Model Deployment
- Creating a Predictive Experiment
- Publishing Your Experiment as a Web Service
- Summary
- ch. 12 Recommendation Systems
- Overview
- Recommendation Systems Approaches and Scenarios
- Business Problem
- Data Acquisition and Preparation
- Dataset
- Training the Model
- Model Testing and Validation
- Summary
- ch. 13 Consuming and Publishing Models on Azure Marketplace
- What Are Machine Learning APIs?
- How to Use an API from Azure Marketplace
- Publishing Your Own Models in Azure Marketplace
- Creating and Publishing a Web Service for Your Machine Learning Model
- Creating Scoring Experiment
- Publishing Your Experiment as a Web Service
- Obtaining the API Key and the Details of the OData Endpoint
- Publishing Your Model as an API in Azure Marketplace
- Summary
- ch. 14 Cortana Analytics
- What Is the Cortana Analytics Suite?
- Capabilities of Cortana Analytics Suite
- Example Scenario
- Summary.