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Saturday, September 15, 2018

The 10 most popular machine learning frameworks used by data scientists | Artificial Intelligence - TechRepublic

Some 90% of data scientists are working on machine learning projects in some capacity, according to a Figure Eight report.

Photo: iStockphoto/monsitj

Data science jobs are among the most coveted careers in America, taking the no. 1 spot on Glassdoor's Best Jobs in America list for the past three years, and boasting high average salaries for those with the right skill set. These professionals report high job satisfaction as well, according to a recent report from Figure Eight: 89% of data scientists said they love their job, up from 67% in 2015.

Demand for data scientists remains high, the report found: 49% of the 240 data scientists surveyed said they get contacted at least once per week for a new job. Part of the reason for this is more companies are expanding their collection and use of data, and need a professional who can parse through it to drive business insights and apply it to new technologies like machine learning and artificial intelligence (AI)...

Here are the top 10 machine learning frameworks used by data scientists, according to the report:
  1. Pandas
  2. Numpy
  3. Scikit-learn
  4. Matplotlib
  5. TensorFlow
  6. Keras
  7. Seaborn
  8. Pytorch & Torch
  9. AWS Deep Learning AMI
  10. Google Cloud ML Engine
It's worth highlighting that many of these popular tools are open source—including Pandas, Numpy, Scikit-learn, Matplotlib, and Tensorflow—indicating that this community prefers open source, community-driven software, the report noted. 
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Source: TechRepublic