Analytics Vidhya

MAY 21, 2021

ArticleVideo Book This article was published as a part of the Data Science Blogathon Introduction Model Building in Machine Learning is an important component of. The post Importance of Cross Validation: Are Evaluation Metrics enough? appeared first on Analytics Vidhya.

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Analytics Vidhya

OCTOBER 11, 2020

This article was published as a part of the Data Science Blogathon. The post HOW TO CHOOSE EVALUATION METRICS FOR CLASSIFICATION MODEL appeared first on Analytics Vidhya. INTRODUCTION Yay!! So you have successfully built your classification model. What should.

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Analytics Vidhya

JUNE 21, 2021

ArticleVideo Book This article was published as a part of the Data Science Blogathon Deep learning techniques like image classification, segmentation, object detection are used. The post Evaluate Your Model – Metrics for Image Classification and Detection appeared first on Analytics Vidhya.

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Analytics Vidhya

OCTOBER 12, 2020

This article was published as a part of the Data Science Blogathon. Introduction Machine learning is about building a predictive model using historical data. The post Quick Guide to Evaluation Metrics for Supervised and Unsupervised Machine Learning appeared first on Analytics Vidhya.

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Machine Learning Metrics Predictive Modeling Data Science 296

Analytics Vidhya

JANUARY 16, 2021

ArticleVideos This article was published as a part of the Data Science Blogathon. Introduction Performance optimization is an important concern in any data science. The post Python Code Performance Measurement – Measure the right metric to optimize better! appeared first on Analytics Vidhya.

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Measurement Metrics Optimization Data Science 238

KDnuggets

APRIL 13, 2023

Improves your data science skill arsenals with these metrics.

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Analytics Vidhya

DECEMBER 2, 2021

This article was published as a part of the Data Science Blogathon. The post Evaluating A Classification Model for Data Science appeared first on Analytics Vidhya. Introduction to Evaluation of Classification Model As the topic suggests we are going to study Classification model evaluation.

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Analytics Vidhya

MAY 19, 2021

ArticleVideo Book This article was published as a part of the Data Science Blogathon Introduction Machine Learning is a branch of Artificial Intelligence. The post Know The Best Evaluation Metrics for Your Regression Model ! It contains. appeared first on Analytics Vidhya.

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Analytics Vidhya

FEBRUARY 4, 2020

The post How to Create a Test Set to Approximate Business Metrics Offline appeared first on Analytics Vidhya. Introduction Most Kaggle-like machine learning hackathons miss a core aspect of a machine learning workflow – preparing an offline evaluation environment while building an.

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KDnuggets

MARCH 31, 2023

Looking to understand the most commonly used distance metrics in machine learning? This guide will help you learn all about Euclidean, Manhattan, and Minkowski distances, and how to compute them in Python.

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datapine

MAY 14, 2019

By gaining the ability to understand, quantify, and leverage the power of online data analysis to your advantage, you will gain a wealth of invaluable insights that will help your business flourish. The ever-evolving, ever-expanding discipline of data science is relevant to almost every sector or industry imaginable – on a global scale.

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Dataiku

DECEMBER 5, 2023

At Everyday AI NYC 2023 , Emma Huang and Cassie Chuljian, senior data scientists at Dataiku, unraveled the secrets behind optimizing model metrics. This blog will go over the seven-step journey they presented to help elevate your data science game.

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Domino Data Lab

MARCH 3, 2021

In some cases, data science does generate models directly to revenue, such as a contextual deal engine that targets people with offers that they can instantly redeem. How do we track value enabled through better decision support such as a data science model or a diagnostic visualization versus an experienced manager making decisions?

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Metrics Modeling Sales Data Science 93

Smart Data Collective

AUGUST 18, 2021

Although widely used, keyword scanning software alone simply doesn’t generate sufficient success metrics when sifting through candidate resumes. If your recruitment process takes longer than this average, data science can help you speed it up while providing better results. Speed up the recruitment process. Retaining staff.

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IBM Big Data Hub

SEPTEMBER 19, 2023

Though you may encounter the terms “data science” and “data analytics” being used interchangeably in conversations or online, they refer to two distinctly different concepts. Meanwhile, data analytics is the act of examining datasets to extract value and find answers to specific questions.

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Data Science Data Analytics Prescriptive Analytics Analytics 84

Data Science 101

FEBRUARY 22, 2020

Welcome to Cloud Data Science 8. Amazon Redshift now supports Authentication with Microsoft Azure AD Redshift, a data warehouse, from Amazon now integrates with Azure Active Directory for login. Thanks for reading the weekly news, and you can find previous editions on the Cloud Data Science News page.

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Analytics Vidhya

JULY 5, 2022

This article was published as a part of the Data Science Blogathon. Introduction to Confusion Matrix In a situation where we want to make discrete predictions, we often wish to assess the quality of our model beyond simple metrics like the model’s accuracy, especially if we have many classes.

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Analytics Vidhya

NOVEMBER 25, 2022

This article was published as a part of the Data Science Blogathon. Source:pixabay.com Introduction State-of-the-art machine learning models and artificially intelligent machines are made of complex processes like adjusting hyperparameters and choosing models that provide better accuracy and the metrics that govern this behavior.

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Smart Data Collective

SEPTEMBER 21, 2021

A growing number of businesses are starting to look for new data-driven approaches to streamline their business models. However, your data-driven business model won’t be very helpful if you don’t focus on the right metrics. Targeting the Right Variables for Your Data-Driven Retail Business Model.

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CIO Business Intelligence

JUNE 29, 2022

In essence, MLOps analysts are a section of the data science team. Whereas data scientists focus on algorithm design and the interpretation of information, MLOps concentrate on making sure the model is working at its best amid changing demands. We monitor algorithms metrics, improve performance, and set best practice. .

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Analytics Vidhya

JANUARY 28, 2022

This article was published as a part of the Data Science Blogathon. Introduction A Machine Learning solution to an unambiguously defined business problem is developed by a Data Scientist ot ML Engineer.

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Analytics Vidhya

OCTOBER 22, 2021

This article was published as a part of the Data Science Blogathon Introduction Working as an ML engineer, it is common to be in situations where you spend hours to build a great model with desired metrics after carrying out multiple iterations and hyperparameter tuning but cannot get back to the same results with the […].

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Analytics Vidhya

NOVEMBER 26, 2021

This article was published as a part of the Data Science Blogathon. We will train various classification models and compare the performance metrics to extract useful insights. In this post, we will discuss the sentiment analysis problem. We have taken the Twitter US airline sentiment dataset for this empirical study.

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Analytics Vidhya

SEPTEMBER 10, 2021

This article was published as a part of the Data Science Blogathon Introduction With ignite, you can write loops to train the network in just a few lines, add standard metrics calculation out of the box, save the model, etc. Well, for those who have moved from TF to PyTorch, we can say that the ignite […].

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Testing Metrics Data Science Publishing 311

IBM Big Data Hub

JULY 6, 2023

While data science and machine learning are related, they are very different fields. In a nutshell, data science brings structure to big data while machine learning focuses on learning from the data itself. What is data science? This post will dive deeper into the nuances of each field.

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Machine Learning Data Science Statistics Deep Learning 60

Domino Data Lab

AUGUST 6, 2021

Data science is an incredibly complex field. Framing data science projects within the four steps of the data science lifecycle (DSLC) makes it much easier to manage limited resources and control timelines, while ensuring projects meet or exceed the business requirements they were designed for.

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KDnuggets

APRIL 19, 2022

Statistical performance metrics aren’t enough to pick the right models to bring to market.

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Modeling Data Science Statistics Metrics 108

IBM Big Data Hub

AUGUST 11, 2023

Because ML is becoming more integrated into daily business operations, data science teams are looking for faster, more efficient ways to manage ML initiatives, increase model accuracy and gain deeper insights. MLOps is the next evolution of data analysis and deep learning. How MLOps will be used within the organization.

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Data Science Machine Learning Cost-Benefit Deep Learning 58

DataRobot Blog

JULY 28, 2022

This article presents a case study of how DataRobot was able to achieve high accuracy and low cost by actually using techniques learned through Data Science Competitions in the process of solving a DataRobot customer’s problem. Sensor Data Analysis Examples. The Best Way to Achieve Both Accuracy and Cost Control.

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Data Science Machine Learning Statistics Modeling 72

Rocket-Powered Data Science

MARCH 19, 2019

A better prescription for business success is for our organization to be analytics – driven and thus analytics-first , while being data -informed and technology -empowered. Analytics are the products, the outcomes, and the ROI of our Big Data , Data Science, AI, and Machine Learning investments!

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Domino Data Lab

JULY 22, 2019

Paco Nathan presented, “Data Science, Past & Future” , at Rev. At Rev’s “ Data Science, Past & Future” , Paco Nathan covered contextual insight into some common impactful themes over the decades that also provided a “lens” help data scientists, researchers, and leaders consider the future.

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Data Science Machine Learning Data Governance Modeling 109

Alation

JULY 28, 2021

As data science processes continue to become operationalized and embedded within business processes, the importance of governing those processes continues to rise. While governance has been a major focus for many years when it comes to managing data, governance focused on data science processes is still far less mature.

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Decision Management Solutions

MARCH 16, 2021

If you are working in data science, you may be frustrated with the progress and ROI of your data science and artificial intelligence projects. The first step in achieving success with data science is recognizing these problem areas in your approach. by James Taylor, CEO, Decision Management Solutions.

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Data Science ROI Machine Learning Modeling 59

Rocket-Powered Data Science

DECEMBER 16, 2018

In a related post we discussed the Cold Start Problem in Data Science — how do you start to build a model when you have either no training data or no clear choice of model parameters. The above example (clustering) is taken from unsupervised machine learning (where there are no labels on the training data).

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Machine Learning Metrics Optimization Data Science 203

KDnuggets

NOVEMBER 9, 2022

Learn these key machine learning performance metrics to ace data science interviews.

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Machine Learning Metrics Data Science 157

Domino Data Lab

JULY 28, 2021

Data science is a field at the convergence of statistics, computer science and business. Its value is so significant that scaling data science has become the new business imperative with organizations spending tens of millions of dollars on data, technology and talent. Data Science Techniques.

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Data Science Machine Learning Statistics Deep Learning 45

O'Reilly on Data

JULY 28, 2020

The first step in building an AI solution is identifying the problem you want to solve, which includes defining the metrics that will demonstrate whether you’ve succeeded. It sounds simplistic to state that AI product managers should develop and ship products that improve metrics the business cares about. Agreeing on metrics.

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