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Explaining black-box models using attribute importance, PDPs, and LIME

Domino Data Lab

The interest in interpretation of machine learning has been rapidly accelerating in the last decade. This can be attributed to the popularity that machine learning algorithms, and more specifically deep learning, has been gaining in various domains. 2015) for additional details. See Wei et al.

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A Guide To Starting A Career In Business Intelligence & The BI Skills You Need

datapine

The Bureau of Labor Statistics also states that in 2015, the annual median salary for BI analysts was $81,320. To simplify things, you can think of back-end BI skills as more technical in nature and related to building BI platforms, like online data visualization tools. This beats projections for almost all other occupations.

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MLOps and the evolution of data science

IBM Big Data Hub

Machine learning (ML), a subset of artificial intelligence (AI), is an important piece of data-driven innovation. Machine learning engineers take massive datasets and use statistical methods to create algorithms that are trained to find patterns and uncover key insights in data mining projects. What is MLOps?

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Towards optimal experimentation in online systems

The Unofficial Google Data Science Blog

the weight given to Likes in our video recommendation algorithm) while $Y$ is a vector of outcome measures such as different metrics of user experience (e.g., Experiments, Parameters and Models At Youtube, the relationships between system parameters and metrics often seem simple — straight-line models sometimes fit our data well.

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Natural Language in Python using spaCy: An Introduction

Domino Data Lab

Data science teams in industry must work with lots of text, one of the top four categories of data used in machine learning. Next let’s use the displaCy library to visualize the parse tree for that sentence: In [4]: from spacy import displacy?? Usually it’s human-generated text, but not always. part of speech.

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Top Challenges and Opportunities for Chief Data Officers

Sisense

But that number rose sharply afterwards, with the team noting there were over 1,000 people in this role by 2015. Platforms like Sisense enable these teams to quickly explore data through code, visualize the results, or convert them to models written back to AWS Redshift or Snowflake.

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Tableau Grows Up!

Rita Sallam

We also learned a lot about Tableau’s roadmap for the next 12 to 24 months in the main keynote from fan favorites, Christian Chabot, founder and now Chairman of the Board, Francois Ajenstat, Chief Product Officer, Andrew Beers, Chief Development Officer and Dan Jewett, VP of Product Management. What’s on Deck for 2017? Enterprise Features.