Within the last decade, we’ve seen companies in every industry leverage big datato become more efficient, save money, and connect with customers. However, the most common uses ofbig dataaren’t the only exciting developments in... Read more.
Biotech, semiconductors, pharmaceuticals, and computer science all have one thing in common: they are high tech industries requiring high levels of expertise, and their career ladders in research and development favor...Read more.
The creation and consumption of data continues to rapidly grow around the globe with large investment in big data analytics hardware, software, and services. The availability of large data sets is one of the core reasons that Deep Learning...Read more.
We’ve covered a few fundamentals and pitfalls of data analytics in our past blog posts. In this blog post, we focus on the four types of data analytics we encounter in data science: Descriptive, Diagnostic, Predictive and Prescriptive...Read more.
Business data, when properly collected and analyzed, can help us do a lot of things. It can help us track the performance of our business. It can help us predict upcomingtrends. It can help usmeet customer needs more effectively, and...Read more.
Opaque and potentially biased mathematical models are remaking our lives—and neither the companies responsible for developing them nor the government is interested in addressing the problem. This week a group of researchers...Read more.
Solving the Titanic Kaggle Competition in Azure ML
In this tutorial we will show you how to complete the titanic Kaggle competition using Microsoft Azure Machine Learning Studio.This video assumes you have an Azure account and you understand how to use Azure.
When: August 23, 2017 Time: 6 pm - 8 pm Where: Data Science Dojo
Modern machine learning libraries make model building look deceptively easy. An unnecessary emphasis (admittedly, annoying to the speaker) on tools like R, Python, SparkML, and techniques like deep learning is prevalent. Relying on tools and techniques while ignoring the fundamentals is the wrong approach to model building.
Unlike most talks these days, this talk is not about deep learning. We will ignore the hype and strictly focus on fundamentals of building robust machine learning models.
Agenda: 6:00 - 6:30 Grab some pizza and socialize 6:30 - 7:45 Presentation 7:45 - 8:00 Questions & Answers
When: August 9, 2017 Time: 10 am - 11:30 am Where: Go-to-Webinar
Modern machine learning libraries make model building look deceptively easy. An unnecessary emphasis (admittedly, annoying to the speaker) on tools like R, Python, SparkML, and techniques like deep learning is prevalent. Relying on tools and techniques while ignoring the fundamentals is the wrong approach to model building.
Unlike most talks these days, this talk is not about deep learning. We will ignore the hype and strictly focus on fundamentals of building robust machine learning models.
Data Science Dojo
2205 152nd Ave NE
Redmond
WA
98052
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