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Cloud Engineering: Creating a Virtual Machine

Introduction: Google Compute Engine lets you create virtual machines running different operating systems, including multiple flavors of Linux (Debian, Ubuntu, Suse, Red Hat, CoreOS) and Windows Server, on Google infrastructure. You can run thousands of virtual CPUs on a system that has been designed to be fast and to offer strong consistency of performance. Here, you’ll learn how to create virtual machine instances of various machine types using the Google Cloud Platform (GCP) Console and using the gcloud command line. You’ll also learn how to connect an NGINX web server to your virtual machine. You should type the commands to reinforce their … Continue reading Cloud Engineering: Creating a Virtual Machine

BigQuery ML(move your model towards data and not data towards model)

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 build models using existing SQL tools and skills. BigQuery ML increases development speed by eliminating the need to move data. BigQuery ML functionality is available by using: The BigQuery web UI The bq command-line tool The BigQuery REST API An external tool such as a Jupyter notebook or business intelligence platform Data Analyst is a Machine learning engineer now? Machine learning on large data sets requires extensive programming and knowledge of ML frameworks. These … Continue reading BigQuery ML(move your model towards data and not data towards model)

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

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

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 … Continue reading Categorize iris flowers using k-means clustering with ML.NET

Introduction to Normal Random Variables: Overview

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

Conditional Probability and Independence Introduction

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 … Continue reading Conditional Probability and Independence Introduction

Probability Rules

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 … Continue reading Probability Rules

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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

Random Variables

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 … Continue reading Random Variables