From PyData Amsterdam, April 2017. Giovanni Lanzani gives a talk on the Data Science Process and where things can go wrong.
(Refreshed Post) Predictive Analytics Made Easy!
Download this free eBook now and see how you can start using Predictive Analytics today to drive business decisions! Read More
This million dollar Kaggle contest is structured into two rounds. In the qualifying round, opening today, you’ll be building a model to improve the Zestimate residual error. The top 100 ranking teams in this round will advance to the final round.
In this video, Riku Inoue and Bryan Lares share how a car auction service and a global insurance company were able to adopt TensorFlow and Cloud Machine Learning to solve real-world business problems and improve customer service and product excellence.
(Reposted due to popular demand) Another great video from Josh Wills. Josh is Sr. Director of Data Science at Cloudera and has a gift for making fairly complicated technology explanations very digestible to the novice and intermediary techie.
What I most love about this video is how Josh explains -very clearly – the issue of translating analytics Machine Learning on a large set of data records (many individuals) and making it work well in a “real life” production environment on a single individual (think eCommerce). Watch Video
“Edward”: A library for probabilistic modeling, inference, and criticism Abstract https://www.meetup.com/NYC-Machine-Learning/events/236943279/ http://www.zentation.com/viewer2/webcast/NAPNgdDUBF/Dustin-Tran—Spotify-Talk-(2017-01-19) Probabilistic modeling is a powerful approach for analyzing empirical information. In this talk, I will provide an overview of Edward, a software library for probabilistic modeling. Formally, Edward is a probabilistic programming system built on computational graphs,supporting compositions of both models and inference […]
In this episode of the O’Reilly Data Show, O’Reilly’s online managing editor Jenn Webb speaks with Natalino Busa on the topic of predictive analytics, the challenges of feature engineering, and a new class of techniques that is enabling features to emerge from patterns within the data.
They also discuss the relationship between predictive techniques and high-quality microservices, and how machine learning is being used to improve financial services. Listen to Podcast
(Re-post) Got a need for speed processing Big Data? In this video talk given at the Apache Flink Meetup in NYC, Slim Baltagi goes over everything you need to know right now about Flink. If you utilize big data analytics, then this is a must watch video!! Enjoy Learn More
The challenge of data modeling is to understand how to work with complex data in order to standardize, structure and optimize data to gets accurate insights quickly. Watch this video by Product Manager, Evan Castle of Sisence, to learn everything you need to know about data modeling. Watch Video