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Hands-On Machine Learning on Google Cloud Platform : Implementing smart and efficient analytics using Cloud ML Engine.
In this book, you will learn how to create powerful machine learning based applications for a wide variety of problems leveraging different data services from the Google Cloud Platform. Finally, you will know the main difficulties that you may encounter and get appropriate strategies to overcome the...
| Clasificación: | Libro Electrónico |
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| Autor principal: | |
| Otros Autores: | , |
| Formato: | Electrónico eBook |
| Idioma: | Inglés |
| Publicado: |
Birmingham :
Packt Publishing,
2018.
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| Temas: | |
| Acceso en línea: | Texto completo |
Tabla de Contenidos:
- Cover; Title Page; Copyright and Credits; Packt Upsell; Contributors; Table of Contents; Preface; Chapter 1: Introducing the Google Cloud Platform; ML and the cloud; The nature of the cloud; Public cloud; Managed cloud versus unmanaged cloud; IaaS versus PaaS versus SaaS; Costs and pricing; ML; Introducing the GCP; Mapping the GCP; Getting started with GCP; Project-based organization; Creating your first project; Roles and permissions; Further reading; Summary; Chapter 2: Google Compute Engine; Google Compute Engine; VMs, disks, images, and snapshots; Creating a VM; Google Shell.
- Google Cloud Platform SDKGcloud; Gcloud config; Accessing your instance with gcloud; Transferring files with gcloud; Managing the VM; IPs; Setting up a data science stack on the VM; BOX the ipython console; Troubleshooting; Adding GPUs to instances; Startup scripts and stop scripts; Resources and further reading; Summary; Chapter 3: Google Cloud Storage; Google Cloud Storage; Box-storage versus drive; Accessing control lists; Access and management through the web console; gsutil; gsutil cheatsheet; Advanced gsutil; Signed URLs; Creating a bucket in Google Cloud Storage.
- Google Storage namespaceNaming a bucket; Naming an object; Creating a bucket; Google Cloud Storage console; Google Cloud Storage gsutil; Life cycle management; Google Cloud SQL; Databases supported; Google Cloud SQL performance and scalability; Google Cloud SQL security and architecture; Creating Google Cloud SQL instances; Summary; Chapter 4: Querying Your Data with BigQuery; Approaching big data; Data structuring; Querying the database; SQL basics; Google BigQuery; BigQuery basics; Using a graphical web UI; Visualizing data with Google Data Studio; Creating reports in Data Studio; Summary.
- Chapter 5: Transforming Your DataHow to clean and prepare the data; Google Cloud Dataprep; Exploring Dataprep console; Removing empty cells; Replacing incorrect values; Mismatched values; Finding outliers in the data; Visual functionality; Statistical information; Removing outliers; Run Job; Scale of features; Min-max normalization; z score standardization; Google Cloud Dataflow; Summary; Chapter 6: Essential Machine Learning; Applications of machine learning; Financial services; Retail industry; Telecom industry; Supervised and unsupervised machine learning.
- Overview of machine learning techniquesObjective function in regression; Linear regression; Decision tree; Random forest; Gradient boosting; Neural network; Logistic regression; Objective function in classification; Data splitting; Measuring the accuracy of a model; Absolute error; Root mean square error; The difference between machine learning and deep learning; Applications of deep learning; Summary; Chapter 7: Google Machine Learning APIs; Vision API; Enabling the API; Opening an instance; Creating an instance using Cloud Shell; Label detection; Text detection; Logo detection.