Predictive Analytics with Microsoft Azure Machine Learning 2nd Edition
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...
| Autores principales: | , , |
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| Formato: | Libro electrónico |
| Idioma: | Inglés |
| Publicado: |
Berkeley, CA :
Apress
2015.
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| Edición: | 2nd ed. 2015. |
| Materias: | |
| Ver en Biblioteca Universitat Ramon Llull: | https://discovery.url.edu/permalink/34CSUC_URL/1im36ta/alma991009629711606719 |
Tabla de Contenidos:
- Contents at a Glance; Contents; About the Authors; About the Technical Reviewers; Acknowledgments; Foreword; Introduction; Part I: Introducing Data Science and Microsoft Azure Machine Learning ; Chapter 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
- The Data Science Process Common Data Science Techniques ; Classification Algorithms; Clustering Algorithms ; Regression Algorithms; Simulation ; Content Analysis; Recommendation Engines ; Cutting Edge of Data Science; The Rise of Ensemble Models; Real-World Applications of Ensemble Models; Building an Ensemble Model; Summary; Bibliography; Chapter 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; Chapter 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; The Curse of Dimensionality; Summary; Chapter 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; Chapter 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
- Part II: Statistical and Machine Learning Algorithms Chapter 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; Part III: Practical Applications ; Chapter 7: Building Customer Propensity Models; The Business Problem ; Data Acquisition and Preparation ; Data Analysis; More Data Treatment; Feature Selection; Training the Model; Model Testing and Validation
- Model Performance
