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Effective Amazon Machine Learning.
Learn to leverage Amazon's powerful platform for your predictive analytics needs About This Book Create great machine learning models that combine the power of algorithms with interactive tools without worrying about the underlying complexity Learn the What's next? of machine learning--mac...
| Clasificación: | Libro Electrónico |
|---|---|
| Autor principal: | |
| Formato: | Electrónico eBook |
| Idioma: | Inglés |
| Publicado: |
Birmingham :
Packt Publishing,
2017.
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| Temas: | |
| Acceso en línea: | Texto completo |
MARC
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| 245 | 1 | 0 | |a Effective Amazon Machine Learning. |
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| 505 | 0 | |a Cover; Copyright; Credits; About the Author; About the Reviewer; www.PacktPub.com; Customer Feedback; Table of Contents; Preface; Chapter 1: Introduction to Machine Learning and Predictive Analytics; Introducing Amazon Machine Learning; Machine Learning as a Service; Leveraging full AWS integration; Comparing performances; Engineering data versus model variety; Amazon's expertise and the gradient descent algorithm; Pricing; Understanding predictive analytics; Building the simplest predictive analytics algorithm; Regression versus classification. | |
| 505 | 8 | |a Expanding regression to classification with logistic regressionExtracting features to predict outcomes; Diving further into linear modeling for prediction; Validating the dataset; Missing from Amazon ML; The statistical approach versus the machine learning approach; Summary; Chapter 2: Machine Learning Definitions and Concepts; What's an algorithm? What's a model?; Dealing with messy data; Classic datasets versus real-world datasets; Assumptions for multiclass linear models; Missing values; Normalization; Imbalanced datasets; Addressing multicollinearity; Detecting outliers. | |
| 505 | 8 | |a Accepting non-linear patternsAdding features?; Preprocessing recapitulation; The predictive analytics workflow; Training and evaluation in Amazon ML; Identifying and correcting poor performances; Underfitting; Overfitting; Regularization on linear models; L2 regularization and Ridge; L1 regularization and Lasso; Evaluating the performance of your model; Summary; Chapter 3: Overview of an Amazon Machine Learning Workflow; Opening an Amazon Web Services Account; Security; Setting up the account; Creating a user; Defining policies; Creating login credentials; Choosing a region. | |
| 505 | 8 | |a Overview of a standard Amazon Machine Learning workflowThe dataset; Loading the data on S3; Declaring a datasource; Creating the datasource; The model; The evaluation of the model; Comparing with a baseline; Making batch predictions; Summary; Chapter 4: Loading and Preparing the Dataset; Working with datasets; Finding open datasets; Introducing the Titanic dataset; Preparing the data; Splitting the data; Loading data on S3; Creating a bucket; Loading the data; Granting permissions ; Formatting the data; Creating the datasource; Verifying the data schema; Reusing the schema. | |
| 505 | 8 | |a Examining data statistics Feature engineering with Athena; Introducing Athena; A brief tour of AWS Athena; Creating a titanic database; Using the wizard; Creating the database and table directly in SQL; Data munging in SQL; Missing values; Handling outliers in the fare; Extracting the title from the name; Inferring the deck from the cabin; Calculating family size; Wrapping up; Creating an improved datasource; Summary; Chapter 5: Model Creation; Transforming data with recipes; Managing variables; Grouping variables; Naming variables with assignments; Specifying outputs. | |
| 500 | |a Data processing through seven transformations. | ||
| 520 | |a Learn to leverage Amazon's powerful platform for your predictive analytics needs About This Book Create great machine learning models that combine the power of algorithms with interactive tools without worrying about the underlying complexity Learn the What's next? of machine learning--machine learning on the cloud--with this unique guide Create web services that allow you to perform affordable and fast machine learning on the cloud Who This Book Is For This book is intended for data scientists and managers of predictive analytics projects; it will teach beginner- to advanced-level machine learning practitioners how to leverage Amazon Machine Learning and complement their existing Data Science toolbox. No substantive prior knowledge of Machine Learning, Data Science, statistics, or coding is required. What You Will Learn Learn how to use the Amazon Machine Learning service from scratch for predictive analytics Gain hands-on experience of key Data Science concepts Solve classic regression and classification problems Run projects programmatically via the command line and the Python SDK Leverage the Amazon Web Service ecosystem to access extended data sources Implement streaming and advanced projects In Detail Predictive analytics is a complex domain requiring coding skills, an understanding of the mathematical concepts underpinning machine learning algorithms, and the ability to create compelling data visualizations. Following AWS simplifying Machine learning, this book will help you bring predictive analytics projects to fruition in three easy steps: data preparation, model tuning, and model selection. This book will introduce you to the Amazon Machine Learning platform and will implement core data science concepts such as classification, regression, regularization, overfitting, model selection, and evaluation. Furthermore, you will learn to leverage the Amazon Web Service (AWS) ecosystem for extended access to data sources, implement realtime predictions, and run Amazon Machine Learning projects via the command line and the Python SDK. Towards the end of the book, you will also learn how to apply these services to other problems, such as text mining, and to more complex datasets. Style and approach This book will include use cases you can relate to. In a very practical manner, you will explore the various capabilities of Amazon Machine Learning services, allowing you to implementing them in your environment with consummate ease. Downloading the ... | ||
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