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Google: Learn cloud skills for free with our new training tracks

ZDNet

Google is offering a free course for people who are on the hunt for skills to use containers, big data and machine-learning models in Google Cloud. The initial batch of courses consists of four tracks aimed at data analysts, cloud architects, data scientists and machine-learning engineers. The January 2021 course offers a fast track to understand key tools for engineers and architects to use in Google Cloud. It includes a series on getting started in Google Cloud, another focussing on its BigQuery data warehouse, one that delves into the Kubernetes engine for managing containers, another for the Anthos application management platform, and a final chapter on Google's standard interfaces for natural language processing and computer vision AI. Participants need to sign up to Google's "skills challenge" and will be given 30 days' free access to Google Cloud labs.



A look at the leading data discovery software and vendors

#artificialintelligence

Turning data into business insight is the ultimate goal. It's not about gathering as much data as possible, it's about applying tools and making discoveries that help a business succeed. The data discovery software market includes a range of software and cloud-based services that can help organizations gain value from their constantly growing information resources. These products fall within the broad BI category, and at their most basic, they search for patterns within data and data sets. Many of these tools use visual presentation mechanisms, such as maps and models, to highlight patterns or specific items of relevance.


Oracle Open World 2017: 9 announcements to follow from autonomous to AI

ZDNet

Oracle's new Autonomous Database Cloud will be cheaper, faster and, with the addition of the Oracle Cyber Security System, safer than anything from Amazon Web Services (AWS). At least that's the assertion Oracle Executive Chairman and CTO Larry Ellison wants everyone to remember from last week's Oracle Open World 2017 (OOW17) event in San Francisco. Whether Oracle's claims are fair and accurate comparisons remains to be seen, as the first release of the Oracle Autonomous Database, through a Data Warehouse Cloud Service, won't be available until December. Count on it being at least a few more months before independent reviewers can do independent tests against rival cloud services. It should be about that same time -- six months from now -- that several other data-related announcements from OOW17 will actually be available.


Intro to text classification with Keras: automatically tagging Stack Overflow posts Google Cloud Big Data and Machine Learning Blog Google Cloud Platform

@machinelearnbot

Posted by Sara Robinson (Developer Advocate), Josh Gordon (Developer Advocate), and Marianne Linhares Monteiro (DA Intern). As humans, our brains can easily read a piece of text and extract the topic, tone, and sentiment. Up until just a few years ago, teaching a computer to do the same thing required extensive machine learning expertise and access to powerful computing resources. Now, frameworks like TensorFlow are helping to simplify the process of building machine learning models, and making it more accessible to developers with no background in ML. In this post, we'll show you how to build a simple model to predict the tag of a Stack Overflow question.


Analyzing customer feedback using machine learning Google Cloud Big Data and Machine Learning Blog Google Cloud Platform

#artificialintelligence

This guest post explains how Wootric's platform uses Google Cloud Natural Language API to complement its own machine learning for saving infrastructure and engineering costs. Wootric is a customer feedback management platform that allows businesses to gauge and quantify customer loyalty through proven feedback metrics such as Net Promoter Score (NPS), Customer Satisfaction (CSAT) and Customer Effort Score (CES). For example, here's an NPS survey that we present in-app (we also support mobile, email and SMS) that usually takes a user less than 30 seconds to complete. As you can see, the question above is very specific and objective. Applying simple arithmetic on this score from your customer base gives you your Net Promoter Score, and allows you to sort your customers into sets of {Promoters, Passives and Detractors}.