Google Cloud Platform Big Data and Machine Learning Fundamentals en Español

Start Date: 07/05/2020

Course Type: Common Course

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About Course

En este curso a pedido y acelerado de 1 semana, los participantes descubrirán las capacidades de los macrodatos y del aprendizaje automático de Google Cloud Platform (GCP). Además, se proporciona una descripción general rápida de Google Cloud Platform y más detalles sobre las capacidades de procesamiento de datos. Al finalizar este curso, los participantes podrán hacer lo siguiente: • Identificar el propósito y el valor de los productos clave de macrodatos y aprendizaje automático disponibles en Google Cloud Platform • Usar Cloud SQL y Cloud Dataproc para migrar las cargas de trabajo existentes de MySQL y Hadoop/Pig/Spark/Hive a Google Cloud Platform • Usar BigQuery y Cloud Datalab para llevar a cabo un análisis de datos interactivo • Elegir entre Cloud SQL, Bigtable y Datastore • Entrenar y usar una red neuronal mediante TensorFlow • Elegir entre los diferentes productos de procesamiento de datos disponibles en Google Cloud Platform Antes de inscribirse en este curso, los participantes deben tener aproximadamente un (1) año de experiencia en uno o más de los siguientes: • Un lenguaje de consulta común, como SQL • Actividades de extracción, transformación y carga • Modelado de datos • Aprendizaje automático o estadísticas • Programación en Python Notas de la Cuenta de Google: • Actualmente, los servicios de Google no están disponibles en China.

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Course Introduction

Este curso acelerado on demand de 1 semana de duración presenta a los participantes las funciones de

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Big Data Bigquery Machine Learning Google Cloud Platform

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Google Cloud Platform Google Cloud Platform is a part of a suite of enterprise services from Google Cloud and provides a set of modular cloud-based services with a host of development tools. For example, hosting and computing, cloud storage, data storage, translations APIs and prediction APIs.
Google Cloud Dataproc Google Cloud Dataproc (Cloud Dataproc) is a cloud-based managed Spark and Hadoop service offered on Google Cloud Platform. Cloud Dataproc utilizes many Google Cloud Platform technologies such as Google Compute Engine and Google Cloud Storage to offer fully managed clusters running popular data processing frameworks such as Apache Hadoop and Apache Spark.
Google Cloud Platform Google Cloud Platform is a cloud computing service by Google that offers hosting on the same supporting infrastructure that Google uses internally for end-user products like Google Search and YouTube. Cloud Platform provides developer products to build a range of programs from simple websites to complex applications.
Google Cloud Dataproc Cloud Dataproc includes many open source packages used for data processing, including items from the Spark and Hadoop ecosystem, and open source tools to connect these frameworks with other Google Cloud Platform products.
Google Cloud Datastore Google Cloud Datastore (Cloud Datastore) is a highly scalable, fully managed NoSQL database service offered by Google on the Google Cloud Platform. Cloud Datastore is built upon Google's Bigtable and Megastore technology.
Information capital Google - Google is working on development of BigQuery - first cloud-based big data processing platform.
SAP Cloud Platform SAP Cloud Platform is a platform as a service developed by SAP SE for creating new applications or extending existing applications in a secure cloud computing environment managed by SAP. The SAP Cloud Platform integrates data and business processes.
Big data Multidimensional big data can also be represented as tensors, which can be more efficiently handled by tensor-based computation, such as multilinear subspace learning. Additional technologies being applied to big data include massively parallel-processing (MPP) databases, search-based applications, data mining, distributed file systems, distributed databases, cloud-based infrastructure (applications, storage and computing resources) and the Internet.
Kidaptive Adaptive Learning platform is a big data/machine learning platform that combines data from multiple learning contexts (digital & physical) and dynamically adjust gameplay as learners engage to help you deliver a personalized learning experience for each child.
Clinical Data, Inc Designed on big data and cloud computing platforms, Clindata claims that it "compresses clinical trial duration by several months, drives significant cost savings, and improves patient safety through predictive analytics and machine learning algorithms."
Third platform Cloud services are at the heart of the third platform. Having big data and mobile devices is one thing, but without the cloud, there will be no way to access this data from outside of the office.
Machine learning Machine learning is closely related to (and often overlaps with) computational statistics, which also focuses on prediction-making through the use of computers. It has strong ties to mathematical optimization, which delivers methods, theory and application domains to the field. Machine learning is sometimes conflated with data mining, where the latter subfield focuses more on exploratory data analysis and is known as unsupervised learning. Machine learning can also be unsupervised and be used to learn and establish baseline behavioral profiles for various entities and then used to find meaningful anomalies.
Machine learning Within the field of data analytics, machine learning is a method used to devise complex models and algorithms that lend themselves to prediction; in commercial use, this is known as predictive analytics. These analytical models allow researchers, data scientists, engineers, and analysts to "produce reliable, repeatable decisions and results" and uncover "hidden insights" through learning from historical relationships and trends in the data.
Google Cloud Print On July 23, 2013 Google updated the service to allow printing from any Windows application, if Google Cloud Printer is installed on the machine. Another new feature is Google Cloud Print Service, which can run as a Windows service so administrators can connect legacy printers to Google Cloud Print in their businesses.
Machine learning Machine Learning poses a host of ethical questions. Systems which are trained on datasets collected with biases may exhibit these biases upon use, thus digitizing cultural prejudices. Responsible collection of data thus is a critical part of machine learning.
Machine learning Leo Breiman distinguished two statistical modelling paradigms: data model and algorithmic model, wherein 'algorithmic model' means more or less the machine learning algorithms like Random forest.
NIWA Cloud Application Platform NIWA Cloud Application Platform is a platform as a service (PaaS) cloud computing platform for developing and hosting web application.
CloudMade Predictive Learning System - a proprietary machine learning platform used by connected car OEMs to enable personalized user experiences. The platform builds on CloudMade’s Smart Data, Hybrid Data and Mapsafe technologies to enable machine learning in any connected car. CloudMade claim that their system is unique because it distributes learning and predictions, using the relatively low cost and powerful cloud systems like Amazon AWS from which to run machine learning processes, whilst carrying out all predictions in the car.
Google Cloud Print Google Cloud Print is a Google service that was created to allow any Cloud-Print-aware application (web, desktop, mobile) on any device in the network cloud to print to any printer – without Google having to create and maintain printing subsystems for all the hardware combinations of client devices and printers, and without the users having to install device drivers to the client, but with documents being fully transmitted to Google. Since July 23, 2013 it also allows printing from any Windows application, if Google Cloud Printer is installed on the machine.
Machine learning Machine learning and data mining often employ the same methods and overlap significantly, but while machine learning focuses on prediction, based on "known" properties learned from the training data, data mining focuses on the discovery of (previously) "unknown" properties in the data (this is the analysis step of Knowledge Discovery in Databases). Data mining uses many machine learning methods, but with different goals; on the other hand, machine learning also employs data mining methods as "unsupervised learning" or as a preprocessing step to improve learner accuracy. Much of the confusion between these two research communities (which do often have separate conferences and separate journals, ECML PKDD being a major exception) comes from the basic assumptions they work with: in machine learning, performance is usually evaluated with respect to the ability to "reproduce known" knowledge, while in Knowledge Discovery and Data Mining (KDD) the key task is the discovery of previously "unknown" knowledge. Evaluated with respect to known knowledge, an uninformed (unsupervised) method will easily be outperformed by other supervised methods, while in a typical KDD task, supervised methods cannot be used due to the unavailability of training data.