KDnuggets

DECEMBER 14, 2023

Looking for the best resources to learn Python programming? Check out these free university courses.

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Smart Data Collective

NOVEMBER 18, 2020

You should learn what a big data career looks like , which involves knowing the differences between different data processes. You should learn what a big data career looks like , which involves knowing the differences between different data processes. What is Data Science? Where to Use Data Science? Where to Use Data Mining?

Data mining 143

Data mining Data Science Informatics Statistics 143

Domino Data Lab

JULY 22, 2019

By looking through this lens, industry is able to ingest and iterate on some current “hot buttons” and infer potential implications on decisions, outcomes, and insights that may surface during the course of their work. Nathan also provided excerpts and insights from recent surveys to help provide additional context. Transcript.

Data Science 109

Data Science Machine Learning Data Governance Modeling 109

Occam's Razor

FEBRUARY 11, 2008

5: Empowerment (The big small company). # It will soon be a year of working at Google and milestones are always a good time for introspection. I have a lot on my mind but there was one thing in particular that I wanted to share with you all: What it is has been like working at Google. This post shares that experience. 1: The brand. +.

Consulting 139

Consulting Visualization Recreation/Entertainment IT 139

Domino Data Lab

FEBRUARY 4, 2019

Spoiler alert: a research field called curiosity-driven learning is emerging at the nexis of experimental cognitive psychology and industry use cases for machine learning, particularly in gaming AI. Of course!!” Introduction. Welcome back to our monthly series about data science. This is not that.

Data Science 66

Data Science Experimentation Data-driven Machine Learning 66

Domino Data Lab

AUGUST 22, 2019

Many thanks to Addison-Wesley Professional for providing the permissions to excerpt “Natural Language Processing” from the book, Deep Learning Illustrated by Krohn , Beyleveld , and Bassens. The excerpt covers how to create word vectors and utilize them as an input into a deep learning model. Introduction.

Deep Learning 111

Deep Learning Modeling Metrics Testing 111