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Recommend Products using ML with Cloud SQL and Dataproc

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

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Dimensionality reduction using sklearn a way of reducing burden

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

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

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

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

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

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

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

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

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Bayes Classification with Cloud Datalab, Spark, and Pig on Google Cloud

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

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Cloud ML Engine Your Friend on cloud

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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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 … Continue reading Analyzing Financial Time Series Using BigQuery and Cloud Datalab