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Cloud ML Engine Your Friend on cloud

SATYAJIT MAITRA

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

SATYAJIT MAITRA

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

SATYAJIT MAITRA

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

SATYAJIT MAITRA

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

SATYAJIT MAITRA

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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Introduction to Normal Random Variables: Overview

SATYAJIT MAITRA

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 … Continue reading Introduction to Normal Random Variables: Overview

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Predict prices using regression with ML.NET

SATYAJIT MAITRA

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 … Continue reading Predict prices using regression with ML.NET

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Hypothesis Testing: Introduction

SATYAJIT MAITRA

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 … Continue reading Hypothesis Testing: Introduction

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The Big Picture: Inference

SATYAJIT MAITRA

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 … Continue reading The Big Picture: Inference