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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 |
Tabla de Contenidos:
- 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.
- 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.
- 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.
- 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.
- 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.