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Cloud IOT Core

Cloud IoT Core is a fully managed service that allows you to easily and securely connect, manage, and ingest data from millions of globally dispersed devices. Cloud IoT Core, in combination with other services on Google Cloud platform, provides a complete solution for collecting, processing, analyzing, and visualizing IoT data in real time to support improved operational efficiency. You will transmit telemetry messages from a device and the device will respond to configuration changes from a server based on real-time data. The devices in this system publish temperature data to their telemetry feeds, and a server consumes the telemetry data … Continue reading Cloud IOT Core

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Query GitHub data using BigQuery

BigQuery is Google’s fully managed, NoOps, low cost analytics database. With BigQuery you can query terabytes of data without needing a database administrator or any infrastructure to manage. BigQuery uses familiar SQL and a pay-only-for-what-you-use charging model. BigQuery allows you to focus on analyzing data to find meaningful insights. In this post we’ll see how to query the GitHub public dataset to grab hands on experience with it. Sign-in to Google Cloud Platform console (console.cloud.google.com) and navigate to BigQuery. You can also open the BigQuery web UI directly by entering the following URL in your browser. Accept the terms of service. … Continue reading Query GitHub data using BigQuery

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Recommend Products using ML with Cloud SQL and Dataproc

As our goal is to provide demo that is why we are using the Cloud SQL or else yo can use spanner for horizontal scaling. our goal is to Create Cloud SQL instance Create database tables by importing .sql files from Cloud Storage Populate the tables by importing .csv files from Cloud Storage Allow access to Cloud SQL Explore the rentals data using SQL statements from CloudShell  the GCP console opens in this tab.Note: You can view the menu with a list of GCP Products and Services by clicking the Navigation menu at the top-left, next to “Google Cloud Platform”.  you populate rentals … Continue reading Recommend Products using ML with Cloud SQL and Dataproc

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Dimensionality reduction using sklearn a way of reducing burden

Principal component analysis (PCA): PCA is used to decompose a multivariate dataset in a set of successive orthogonal components that explain a maximum amount of the variance. In scikit-learn, PCA is implemented as a transformer object that learns n components in its fit method, and can be used on new data to project it on these components. PCA centers but does not scale the input data for each feature before applying the SVD. The optional parameter parameter whiten=True makes it possible to project the data onto the singular space while scaling each component to unit variance. The PCA object also provides a probabilistic interpretation of the PCA that can give a likelihood … Continue reading Dimensionality reduction using sklearn a way of reducing burden

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Machine Learning crash course (Tensorflow Examples)

machine learning comes with the learning pattern which is supervised learning at a first glance .so here is a brief about it terms used here are : the very first thing needs to keep in mind is framing your machine learning model/projects means what you want to achieve out of the data. example may contains as follows: A regression model predicts continuous values. For example, regression models make predictions that answer questions like the following: What is the value of a house in California? What is the probability that a user will click on this ad? A classification model predicts discrete values. For example, … Continue reading Machine Learning crash course (Tensorflow Examples)

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Spark Cluster Overview

Apache Spark is a fast and general-purpose cluster computing system. It provides high-level APIs in Java, Scala, Python and R, and an optimized engine that supports general execution graphs. It also supports a rich set of higher-level tools including Spark SQL for SQL and structured data processing, MLlib for machine learning, GraphX for graph processing, and Spark Streaming. Security in Spark is OFF by default. This could mean you are vulnerable to attack by default. Spark uses Hadoop’s client libraries for HDFS and YARN.  Users can also download a “Hadoop free” binary and run Spark with any Hadoop version by augmenting Spark’s classpath. Scala and Java users can … Continue reading Spark Cluster Overview

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Be different build a machine learning model with some extra line in your SQL query and grab attention

By the introduction you probably get it and yes we are talking about Biguery ML . BigQuery ML enables users to create and execute machine learning models in BigQuery using standard SQL queries. BigQuery ML democratizes machine learning by enabling SQL practitioners to build models using existing SQL tools and skills. BigQuery ML increases development speed by eliminating the need to move data. SEND FEEDBACK BigQuery ML  Documentation Introduction to BigQuery ML Overview BigQuery ML enables users to create and execute machine learning models in BigQuery using standard SQL queries. BigQuery ML democratizes machine learning by enabling SQL practitioners to … Continue reading Be different build a machine learning model with some extra line in your SQL query and grab attention

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Build A Tool in the Google docs that read the sentiment of your document by using Google’s Natural Language API

The Natural Language API is a pretrained machine learning model that can analyze syntax, extract entities, and evaluate the sentiment of text. It can be called from Google Docs to perform all of these functions. This post will walk you through calling the Natural Language API to recognize the sentiment of selected text in a Google Doc and highlight it based on that sentiment. What are we going to be building? Once this post is complete, you will be able to select text in a document and mark its sentiment, using a menu choice, as shown below. Text will be highlighted in … Continue reading Build A Tool in the Google docs that read the sentiment of your document by using Google’s Natural Language API

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Build simple Apps that can convert text-to-speech and speech-to-text but in c#

As a developer back in 2017 I always wonder it will be nice to write Machine learning code in c# .Net framework to show my manager that i know enough to become Team Lead but past is past and i left that productive company most of the company manager’s in the world are same full with dull insights as they tries to bring people down and demotivate them from their goal as they didn’t get their anyways the other day i was searching memes in the internet and all of a sudden one of the website gives me two HD … Continue reading Build simple Apps that can convert text-to-speech and speech-to-text but in c#

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Become A Marketing Expert By using Google Cloud products learn the Art of Asking with a Browser

First thing first We will discuss what is Bigquery and why we are choosing Bigquery….. BigQuery is Google’s fully managed, NoOps, low cost analytics database. With BigQuery you can query terabytes and terabytes of data without having any infrastructure to … Continue reading Become A Marketing Expert By using Google Cloud products learn the Art of Asking with a Browser

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Bayes Classification with Cloud Datalab, Spark, and Pig on Google Cloud

Note: If you are really following with post this job can take upto 1:30 hours to finish and if you stuck in a typo it will increase your resistance power In this post you will learn how to deploy a … Continue reading Bayes Classification with Cloud Datalab, Spark, and Pig on Google Cloud

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Building an IoT Analytics Pipeline on Google Cloud Platform step by step

let’s start with the definition of IoT: The term Internet of Things (IoT) refers to the interconnection of physical devices with the global Internet. These devices are equipped with sensors and networking hardware, and each is globally identifiable. Taken together, these capabilities … Continue reading Building an IoT Analytics Pipeline on Google Cloud Platform step by step

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Cloud ML Engine Your Friend on cloud

What we are doing here. Theory of Not relativity but cloud ml engine a bit of tensorflow(not stack overflow) and hands on in Create a TensorFlow training application and validate it locally. Run your training job on a single worker instance in the cloud. Run your training job as a distributed training job in the cloud. Optimize your hyperparameters by using hyperparameter tuning. Deploy a model to support prediction. Request an online prediction and see the response. Request a batch prediction. What We are building here: a wide and deep model for predicting income category based on United States Census … Continue reading Cloud ML Engine Your Friend on cloud

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Visualizing BigQuery data in a Jupyter notebook with SQL

BigQuery is a petabyte-scale analytics data warehouse that you can use to run SQL queries over vast amounts of data in near realtime. Data visualization tools can help you make sense of your BigQuery data and help you analyze the data interactively. You can use visualization tools to help you identify trends, respond to them, and make predictions using your data. In this tutorial, you use the BigQuery Python client library and Pandas in a Jupyter notebook to visualize data in the BigQuery natality sample table. SEND FEEDBACK BigQuery Visualizing BigQuery data in a Jupyter notebook Contents Objectives Costs Before you begin Setting … Continue reading Visualizing BigQuery data in a Jupyter notebook with SQL

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Analyzing Financial Time Series Using BigQuery and Cloud Datalab

This solution illustrates the power and utility of BigQuery and Cloud Datalab as tools for quantitative analysis. The solution provides an introduction (this document) and gets you set up to run a notebook-based Cloud Datalab tutorial. If you’re a quantitative analyst, you use a … Continue reading Analyzing Financial Time Series Using BigQuery and Cloud Datalab

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A/B testing

The A/B test (also known as a randomised controlled trial, or RCT, in the other sciences) is a powerful tool for product development. some motivations: With the rise of digital marketing led by tools including Google Analytics, Google Adwords, and Facebook Ads, a key competitive advantage for businesses is using A/B testing to determine effects of digital marketing efforts. Why? In short, small changes can have big effects. This is why A/B testing is a huge benefit. A/B Testing enables us to determine whether changes in landing pages, popup forms, article titles, and other digital marketing decisions improve conversion rates … Continue reading A/B testing

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Binomial Random Variables: Introduction

Binomial Random Variables So far, in our discussion about discrete random variables, we have been introduced to: The probability distribution, which tells us which values a variable takes, and how often it takes them. The mean of the random variable, which tells us the long-run average value that the random variable takes. The standard deviation of the random variable, which tells us a typical (or long-run average) distance between the mean of the random variable and the values it takes. We will now introduce a special class of discrete random variables that are very common, because as you’ll see, they … Continue reading Binomial Random Variables: Introduction

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How To Distribute Sample

Sampling Distributions Introduction Already on several occasions we have pointed out the important distinction between a population and a sample. In Exploratory Data Analysis, we learned to summarize and display values of a variable for a sample, such as displaying the blood types of 100 randomly chosen U.S. adults using a pie chart, or displaying the heights of 150 males using a histogram and supplementing it with the sample mean (X¯) and sample standard deviation (S). In our study of Probability and Random Variables, we discussed the long-run behavior of a variable, considering the population of all possible values taken by that variable. For example, we … Continue reading How To Distribute Sample

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TensorFlow Machine Learning on the Amazon Deep Learning AMI

TensorFlow is a popular framework used for machine learning. The Amazon Deep Learning AMI comes bundled with everything you need to start using TensorFlow from development through to production. In this post, you will develop, visualize, serve, and consume a TensorFlow machine learning model using the Amazon Deep Learning AMI.  Objectives Upon completion of this post you will be able to: Create machine learning models in TensorFlow Visualize TensorFlow graphs and the learning process in TensorBoard Serve trained TensorFlow models with TensorFlow Serving Create clients that consume served TensorFlow models, all with the Amazon Deep Learning AMI Prerequisites You should be familiar … Continue reading TensorFlow Machine Learning on the Amazon Deep Learning AMI

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Probability A short story

Sample Spaces As we saw in the previous section, probability questions arise when we are faced with a situation that involves uncertainty. Such a situation is called a random experiment, an experiment that produces an outcome that cannot be predicted in advance (hence the uncertainty). Here are a few examples of random experiments: Toss a coin once and record whether you get heads (H) or tails (T). The possible outcomes that this random experiment can produce are: {H, T}. Toss a coin twice. The possible outcomes that this random experiment can produce are: {HH, HT, TH, TT}. Toss a coin 3 … Continue reading Probability A short story

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Causation and Lurking Variables With simpson’s paradox

The one and only principle rule in statistics is Principle:Association does not imply causation! The scatterplot below illustrates how the number of firefighters sent to fires (X) is related to the amount of damage caused by fires (Y) in a certain city. The scatterplot clearly displays a fairly strong (slightly curved) positive relationship between the two variables. Would it, then, be reasonable to conclude that sending more firefighters to a fire causes more damage, or that the city should send fewer firefighters to a fire, in order to decrease the amount of damage done by the fire? Of course not! So what is going … Continue reading Causation and Lurking Variables With simpson’s paradox

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Data Raconteur Suresh Convince his Wife Reshmi that chennai is becoming dry ……

Hi this is suresh working as a software engineer in a HUGE MNC for past 7 years based on chennai but recently i used to work from home for the same company but my wife is much scared of it as she thinks my job is lost and we have to starved to death but how can I told you that we are gonna die but not due to starvation but due to thrust so I decided to tell her the story of my city which is becoming dry with data as now even she goes to grocery shopping she … Continue reading Data Raconteur Suresh Convince his Wife Reshmi that chennai is becoming dry ……

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Scales Of Measurement:

 The four different scales of measurement, from least to most precise, are Nominal Ordinal Interval Ratio Nominal: The nominal scale of measurement is a qualitative measure that uses discrete categories to describe a characteristic of the research participants. For each participant, the researcher determines the presence, absence, and type of the attribute. Nominal scales of measurement may have two categories, such as citizen status (citizen/non-citizen), or they can have more than two categories, like religious affiliation (e.g., Agnostic, Buddhist, Jewish, Muslim) or marital status (e.g., divorced, married, single). Often, as described here, the categories have names; however, researchers code them with numbers … Continue reading Scales Of Measurement:

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Machine Learning Applications for Big Data(Regression):

Machine Learning: Machine learning is an application of artificial intelligence (AI) that provides systems the ability to automatically learn and improve from experience without being explicitly programmed. Machine learning focuses on the development of computer programs that can access data and use it … Continue reading Machine Learning Applications for Big Data(Regression):

Explore a BigQuery Public Dataset

Storing and querying massive datasets can be time consuming and expensive without the right hardware and infrastructure. Google BigQuery is an enterprise data warehouse that solves this problem by enabling super-fast SQL queries using the processing power of Google’s infrastructure. Simply move your data into BigQuery and let us handle the hard work. You can control access to both the project and your data based on your business needs, such as giving others the ability to view or query your data. You access BigQuery through the GCP Console, the command-line tool, or by making calls to the BigQuery REST API using a variety of client libraries such … Continue reading Explore a BigQuery Public Dataset

Open Economics For understanding Banking Domain and trade system and building ML tools

For building tools related to Financial trade analysis understanding the domain is important here we will try to disclose the basic of it and that completes your technical knowledge along with domain expertise . Normally we assume that Economy of a Capitalist country to be closed. This is done to simplified calculations of country’s GDP,GNP,NI,Wage etc but in reality this never happens so lets just jump into foundation of open economics. Interaction of economics with world happens in three broad ways . Goods that consumer and Firms can choose between state and foreign.Example will be choosing a Books from local … Continue reading Open Economics For understanding Banking Domain and trade system and building ML tools