A workspace is a context for the experiments, data, compute targets, and other assets associated with a machine learning workload. Workspaces for Machine Learning Assets A workspace defines the boundary for a set of related machine learning assets. You can use workspaces to group machine learning assets based on projects, deployment environments (for example, test…

# Tag: data science

## Predict prices using regression with ML.NET

You can follow along or download the source code here. 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,…

## Analyzing logs in real time using Fluentd and BigQuery

This tutorial shows how to log browser traffic and analyze it in real time. This is useful when you have a significant amount of logging from various sources and you want to debug issues or generate up-to-date statistics from the logs. The tutorial describes how to send log information generated by an NGINX web server to BigQuery…

## 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…

## 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 variety of tools and techniques to mine big data, such as market transaction histories, for…

## Analysis Of Public health data with R with Logistic Regression

As we are talking about logistic regression to be used in place of linear regression some points needs to keep in mind while we are using it. Why does linear regression not work with binary outcomes? Binary outcomes only have two values. The example we are using throughout this course is diabetes, where individuals either have…

## ML Strategy for Machine Learning Projects

This post is inspired by Andrew Ng’s deep learning specialization so if you are really interested in this project do check the course. So here comes the questions why ML stratergy? well the answer can be pretty motivating suppose you are working on a criminal identification app via deep learning and you are getting on…

## Running Spark on Azure Databricks

Who Should See This Blog Post? This course is intended for the people who wants to Run their Analytics workload and Machine Learning workload on DataBricks And chosen Azure as their distributor. Skills Required To Follow: SQL Machine Learning Why we need DataBricks and Azure services ….. well we can train one model on DataBricks…

## A Practical Approach To Stochastic gradient descent(SGD)

Stochastic gradient descent (often abbreviated SGD) is an iterative method for optimizing an objective function with suitable smoothness properties (e.g. differentiable or subdifferentiable). It is called stochastic because the method uses randomly selected (or shuffled) samples to evaluate the gradients, hence SGD can be regarded as a stochastic approximation of gradient descent optimization. Background Both statistical estimation and machine learning consider the problem of minimizing an objective function that has the form of a sum:{\displaystyle Q(w)={\frac {1}{n}}\sum _{i=1}^{n}Q_{i}(w),} where the parameter w that…

## How To Think Like A Data Scientist

A data scientist is some one who will take your data do some data work and show you insights or to be fair the relationship between different parameter of your data, here then you can use this insights to make some of your work prediction by appling ML models and that is what a data…