Sticky post
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
Sticky post
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
Sticky post
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
Sticky post
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
Sticky post
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
Sticky post
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
Sticky post
Apache Spark™ is a unified analytics engine for large-scale data processing. 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 … Continue reading detecting fraud with decision tree and spark
Sticky post
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
Sticky post
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
Sticky post
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
Sticky post
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
Sticky post
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
Sticky post
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
Sticky post
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 ……
Sticky post
We have discussed that an image is nothing but a logical array with number in each pixel representing the intensity of light in each pixel . And we can think it as numpy array and to work with it we … Continue reading Drawing With OpenCV
Sticky post
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:
Sticky post
this post is inspired by stanford.edu The process of statistics starts when we identify what group we want to study or learn something about. We call this group the population. Note that the word populationhere (and in the entire course) does not … Continue reading The Big Picture of Statistics
Sticky post
The First ever question should be and it is what is cloud computing well there is a clear answer of that After basic definition of cloud let’s see the google’s offering in cloud concepts of Zone and Region in Nutshell: … Continue reading Cloud Architecting With GCP
Sticky post
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):
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 … Continue reading How To Set up a Development Environment in Python in Google Cloud
