Remove Data Science Remove Metrics Remove Predictive Modeling
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Quick Guide to Evaluation Metrics for Supervised and Unsupervised Machine Learning

Analytics Vidhya

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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Data Insights for Everyone — The Semantic Layer to the Rescue

Rocket-Powered Data Science

The way that I explained it to my data science students years ago was like this. The semantic layer delivers data insights discovery and usability across the whole enterprise, with each business user empowered to use the terminology and tools that are specific to their role. That’s data democratization. That’s empowering.

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Top 10 Data Innovation Trends During 2020

Rocket-Powered Data Science

2) MLOps became the expected norm in machine learning and data science projects. MLOps takes the modeling, algorithms, and data wrangling out of the experimental “one off” phase and moves the best models into deployment and sustained operational phase. And the goodness doesn’t stop there.

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Humans-in-the-loop forecasting: integrating data science and business planning

The Unofficial Google Data Science Blog

by THOMAS OLAVSON Thomas leads a team at Google called "Operations Data Science" that helps Google scale its infrastructure capacity optimally. Over the life of the forecast, the data scientist will publish historical accuracy metrics. Our team does a lot of forecasting.

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Why you should care about debugging machine learning models

O'Reilly on Data

If a model is going to be used on all kinds of people, it’s best to ensure the training data has a representative distribution of all kinds of people as well. Interpretable ML models and explainable ML. The debugging techniques we propose should work on almost any kind of ML-based predictive model.

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Top 10 Analytics And Business Intelligence Trends For 2020

datapine

Hotels try to predict the number of guests they can expect on any given night in order to adjust prices to maximize occupancy and increase revenue. There are plenty of big data examples used in real life, shaping our world, be it in the buying experience or managing customers’ data.

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Data science vs data analytics: Unpacking the differences

IBM Big Data Hub

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.