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  • Cloud IOT Core

    November 9, 2019 by

    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… Read more Continue reading Cloud IOT Core

  • Query GitHub data using BigQuery

    November 9, 2019 by

    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.… Read more Continue reading Query GitHub data using BigQuery

  • Recommend Products using ML with Cloud SQL and Dataproc

    November 9, 2019 by

    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… Read more Continue reading Recommend Products using ML with Cloud SQL and Dataproc

  • Dimensionality reduction using sklearn a way of reducing burden

    November 8, 2019 by

    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… Read more Continue reading Dimensionality reduction using sklearn a way of reducing burden

  • Machine Learning crash course (Tensorflow Examples)

    October 21, 2019 by

    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,… Read more Continue reading Machine Learning crash course (Tensorflow Examples)

  • Spark Cluster Overview

    September 11, 2019 by

    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… Read more Continue reading Spark Cluster Overview

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

    September 6, 2019 by

    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… Read more Continue reading Be different build a machine learning model with some extra line in your SQL query and grab attention

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

    September 1, 2019 by

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

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

    August 30, 2019 by

    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… Read more Continue reading Build simple Apps that can convert text-to-speech and speech-to-text but in c#

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

    August 12, 2019 by

    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… Read more Continue reading Become A Marketing Expert By using Google Cloud products learn the Art of Asking with a Browser

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

    July 27, 2019 by

    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… Read more Continue reading Building an IoT Analytics Pipeline on Google Cloud Platform step by step

  • Cloud ML Engine Your Friend on cloud

    July 26, 2019 by

    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… Read more Continue reading Cloud ML Engine Your Friend on cloud

  • Visualizing BigQuery data in a Jupyter notebook with SQL

    July 24, 2019 by

    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… Read more Continue reading Visualizing BigQuery data in a Jupyter notebook with SQL

  • A/B testing

    July 22, 2019 by

    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… Read more Continue reading A/B testing

  • Binomial Random Variables: Introduction

    July 21, 2019 by

    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… Read more Continue reading Binomial Random Variables: Introduction

  • How To Distribute Sample

    July 18, 2019 by

    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… Read more Continue reading How To Distribute Sample

  • TensorFlow Machine Learning on the Amazon Deep Learning AMI

    July 18, 2019 by

    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… Read more Continue reading TensorFlow Machine Learning on the Amazon Deep Learning AMI

  • Probability A short story

    July 17, 2019 by

    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… Read more Continue reading Probability A short story

  • Causation and Lurking Variables With simpson’s paradox

    July 15, 2019 by

    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… Read more Continue reading Causation and Lurking Variables With simpson’s paradox

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

    July 11, 2019 by

    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… Read more Continue reading Data Raconteur Suresh Convince his Wife Reshmi that chennai is becoming dry ……

  • Scales Of Measurement:

    July 10, 2019 by

     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… Read more Continue reading Scales Of Measurement:

  • Explore a BigQuery Public Dataset

    November 9, 2019 by

    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… Read more Continue reading Explore a BigQuery Public Dataset

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

    November 7, 2019 by

    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… Read more Continue reading Open Economics For understanding Banking Domain and trade system and building ML tools

  • Categorize iris flowers using k-means clustering with ML.NET

    August 26, 2019 by

    This tutorial illustrates how to use ML.NET to build a clustering model for the iris flower data set. In this tutorial, you learn how to: Understand the problem Select the appropriate machine learning task Prepare the data Load and transform the data Choose a learning algorithm Train the model Use the model for predictions Prerequisites Visual Studio 2017 15.6 or later with the “.NET Core cross-platform development” workload installed. Understand the problem This problem is about dividing the set of iris flowers in different groups based on the flower features. Those features are the length and width of a sepal and the length and… Read more Continue reading Categorize iris flowers using k-means clustering with ML.NET

  • How To Set up a Development Environment in Python in Google Cloud

    August 2, 2019 by

    In this blog, you set up a Python development environment on Google Cloud Platform, using Google Compute Engine to create a virtual machine (VM) and installing software libraries for software development. You perform the following tasks: Provision a Google Compute Engine instance. Connect to the instance using SSH. Install a Python library on the instance. Verify the software installation. Compute Engine is just one resource provided on Google Cloud Platform. Google Cloud Platform Google Cloud Platform (GCP) consists of a set of physical assets, such as computers and hard disk drives, and virtual resources, such as virtual machines (VMs), that… Read more Continue reading How To Set up a Development Environment in Python in Google Cloud

  • Introduction to Normal Random Variables: Overview

    July 20, 2019 by

    In the Exploratory Data Analysis sections of this course, we encountered data sets, such as lengths of human pregnancies, whose distributions naturally followed a symmetric unimodal bell shape, bulging in the middle and tapering off at the ends. Many variables, such as pregnancy lengths, shoe sizes, foot lengths, and other human physical characteristics exhibit these properties: symmetry indicates that the variable is just as likely to take a value a certain distance below its mean as it is to take a value that same distance above its mean; the bell-shape indicates that values closer to the mean are more likely, and it… Read more Continue reading Introduction to Normal Random Variables: Overview

  • Predict prices using regression with ML.NET

    July 20, 2019 by

    Create a console application Create a .NET Core Console Application called “TaxiFarePrediction”. Create a directory named Data in your project to store the data set and model files. Install the Microsoft.ML NuGet Package:In Solution Explorer, right-click the project and select Manage NuGet Packages. Choose “nuget.org” as the Package source, select the Browse tab, search for Microsoft.ML, select the package in the list, and select the Install button. Select the OK button on the Preview Changesdialog and then select the I Accept button on the License Acceptance dialog if you agree with the license terms for the packages listed. Do the same for the Microsoft.ML.FastTree Nuget package. Prepare and understand the data Download the taxi-fare-train.csv and the taxi-fare-test.csv data sets and save them to the Datafolder you’ve created… Read more Continue reading Predict prices using regression with ML.NET

  • Hypothesis Testing: Introduction

    July 20, 2019 by

    We are now moving to the other kind of inference, hypothesis testing. We say that hypothesis testing is “the other kind” because, unlike the inferential methods we presented so far, where the goal was estimating the unknown parameter, the idea, logic and goal of hypothesis testing are quite different. In the first part of this section we will discuss the idea behind hypothesis testing, explain how it works, and introduce new terminology that emerges in this form of inference. The next two parts will be more specific and will discuss hypothesis testing for the population proportion (p), and for the population mean (μ). General… Read more Continue reading Hypothesis Testing: Introduction

  • The Big Picture: Inference

    July 20, 2019 by

    Recall again the Big Picture, the four-step process that encompasses statistics: data production, exploratory data analysis, probability, and inference. We are about to start the fourth part of the process and the final section of this course, where we draw on principles learned in the other units (exploratory data analysis, producing data, and probability) in order to accomplish what has been our ultimate goal all along: use a sample to infer (or draw conclusions) about the population from which it was drawn. The specific form of inference called for depends on the type of variables involved—either a single categorical or quantitative… Read more Continue reading The Big Picture: Inference

  • Binary Imbalanced Learning A practical Approach in R

    July 19, 2019 by

     Introduction and motivation Binary classification problem is arguably one of the simplest and most straightforward problems in Machine Learning. Usually we want to learn a model trying to predict whether some instance belongs to a class or not. It has many practical applications ranging from email spam detection to medical testing (determine if a patient has a certain disease or not). Slightly more formally, the goal of binary classification is to learn a function f(x) that map x (a vector of features for an instance/example) to a predicted binary outcome ŷ (0 or 1). Most classification algorithms, such as logistic regression, Naive Bayes and decision trees,… Read more Continue reading Binary Imbalanced Learning A practical Approach in R

  • Conditional Probability and Independence Introduction

    July 19, 2019 by

    Introduction In the last section, we established the five basic rules of probability, which include the two restricted versions of the Addition Rule and Multiplication Rule: The Addition Rule for Disjoint Events and the Multiplication Rule for Independent Events. We have also established a General Addition Rule for which the events need not be disjoint. In order to complete our set of rules, we still require a General Multiplication Rule for which the events need not be independent. In order to establish such a rule, however, we first need to understand the important concept of conditional probability. This section will be organized as follows: We’ll first… Read more Continue reading Conditional Probability and Independence Introduction

  • Probability Rules

    July 18, 2019 by

    Basic Probability Rules In the previous section we considered situations in which all the possible outcomes of a random experiment are equally likely, and learned a simple way to find the probability of any event in this special case. We are now moving on to learn how to find the probability of events in the general case (when the possible outcomes are not necessarily equally likely), using five basic probability rules. Fortunately, these basic rules of probability are very intuitive, and as long as they are applied systematically, they will let us solve more complicated problems; in particular, those problems… Read more Continue reading Probability Rules

  • Random Variables

    July 17, 2019 by

    In the previous sections we’ve learned principles and tools that help us find probabilities of events in general. Now that we’ve become proficient at doing that, we’ll talk about random variables. Just like any other variable, random variables can take on multiple values. What differentiates random variables from other variables is that the values for these variables are determined by a random trial, random sample, or simulation. The probabilities for the values can be determined by theoretical or observational means. Such probabilities play a vital role in the theory behind statistical inference, our ultimate goal in this course. Introduction We first… Read more Continue reading Random Variables

  • Blind and Double-Blind Experiments

    July 16, 2019 by

    Suppose the experiment about methods for quitting smoking were carried out with randomized assignments of subjects to the four treatments, and researchers determined that the percentage succeeding with the combination drug/therapy method was highest, and the percentage succeeding with no drugs or therapy was lowest. In other words, suppose there is clear evidence of an association between method used and success rate. Could it be concluded that the drug/therapy method causes success more than trying to quit without using drugs or therapy? Perhaps. Although randomized controlled experiments do give us a better chance of pinning down the effects of the… Read more Continue reading Blind and Double-Blind Experiments

  • Designing Studies Introduction

    July 15, 2019 by

    Designing Studies Now that we have learned about the first stage of data production— sampling—we can move on to the next stage—designing studies. If haven’t read about it read from here. Introduction Obviously, sampling is not done for its own sake. After this first stage in the data production process is completed, we come to the second stage, that of gaining information about the variables of interest from the sampled individuals. In this module we’ll discuss three study designs; each design enables you to determine the values of the variables in a different way. You can: – Carry out an observational… Read more Continue reading Designing Studies Introduction

  • Producing Data From Population

    July 15, 2019 by

    Recall “The Big Picture,” the four-step process that encompasses statistics: data production, exploratory data analysis, probability, and inference. In the previous posts, we considered exploratory data analysis—the discovery of patterns in the raw data. First we need to choose the individuals from the population that will be included in the sample. Then, once we have chosen the individuals, we need to collect data from them. The first stage is called sampling, and the second stage is called study design. As we have seen, exploratory data analysis seeks to illuminate patterns in the data by summarizing the distributions of quantitative or categorical variables,… Read more Continue reading Producing Data From Population

  • Computer Vision Basics

    July 10, 2019 by

    Loading, displaying, and saving images with opencv Computer vision is an interdisciplinary scientific field that deals with how computers can be made to gain high-level understanding from digital images or videos. From the perspective of engineering, it seeks to automate tasks that the human visual system can do. OpenCV is a library of programming functions mainly aimed at real-time computer vision. Originally developed by Intel, it was later supported by Willow Garage then Itseez. The library is cross-platform and free for use under the open-source BSD license. Now as you are aware of opencv and CV at the same time let’s get started with computer… Read more Continue reading Computer Vision Basics

  • What is Computer Vision An Introduction

    July 9, 2019 by

    Computer vision is an interdisciplinary scientific field that deals with how computers can be made to gain high-level understanding from digital images or videos. From the perspective of engineering, it seeks to automate tasks that the human visual system can do. what computer vision consist of : Artificial intelligence: Areas of artificial intelligence deal with autonomous planning or deliberation for robotical systems to navigate through an environment. A detailed understanding of these environments is required to navigate through them. Information about the environment could be provided by a computer vision system, acting as a vision sensor and providing high-level information about the environment and the robot. Artificial intelligence… Read more Continue reading What is Computer Vision An Introduction

  • Ceiling Analysis in Machine Learning?

    July 9, 2019 by

    Ceiling Analysis is a way to systematically find the weakest component of your system, and therefore optimising that weakest component would best serve your time to bring the greatest improvement to the overall system. Why it is important in case of deep learning? Ceiling analysis is the process of manually overriding each component in your system to provide 100% accurate predictions with that component. Thereafter, you can observe the overall improvement of your deep learning system component by component. For photo OCR example, this is what we might have: Component Accuracy Note Overall system 72% Text Detection (TD) 89% Make… Read more Continue reading Ceiling Analysis in Machine Learning?

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