Online Course | “Understanding Data Science” with Doug Rose (via Lynda) By Chris McCabe Last weekend I took the Lynda course “Understanding Data Science ” with Doug Rose. The class is designed for those that do not intend to be Data Scientists but want to become familiar with the process and terminology. The class has […]
Great (free) Machine Learning course for beginners by Caltech University. Introduction to; supervised, unsupervised, and reinforcement learning. Components of the learning problem. Lectures 1 of 18 of Caltech’s Machine Learning Course – CS 156 by Professor Yaser Abu-Mostafa. Watch Video
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
A FREE Masters in Data Science. More and more people are learning on-line via the flood of excellent “open source” resources of classes, ebooks, software, etc. Clare Corthell has created a website to allow anybody to take virtually the same curriculum offered for a Masters in Data Science for Free.
Will it be an official Masters? No, but an official Masters is not always what is needed. Often its the knowledge and experience working with the tools and techniques necessary to actually do Data Science. For some, this free curriculum will allow business-line leaders, Analysts and Programmers from other fields to fill in the education gaps and get better at their job, as well as, one step closer to being an actual Data Scientist. Read More
Apache Spark’s popularity as part of big data analytics solutions is exploding. Spark is an open-source data analytics cluster computing framework originally developed in the AMPLab at UC Berkeley. Spark fits into the Hadoop open-source community, building on top of the Hadoop Distributed File System (HDFS). However, Spark promises performance up to 100 times faster than Hadoop MapReduce for certain applications…and that’s why you should care!
Spark’s in-memory cluster computing is very well suited to machine learning algorithms. These Videos will give you a nice introduction to Spark, how it’s being used in business and why you should care…Watch Spark Videos…
VIDEO PODCAST | In Episode 6 and 7 of this podcast series by Renee Teate of “Becoming a Data Scientist”, interviews Erin Shellman and Enda Ridge about how they became Data Scientists and what they do on the job. Read More
VIDEO PODCAST | In Episode 1 of this podcast series by Renee Teate of “Becoming a Data Scientist”, she interviews Will Kurt, who talks about his path from English & Literature and Library & Information Science degrees to becoming the Lead Data Scientist at KISSmetrics. Read More
DAY 2 | Spark Summit East 2016 took place last month in NYC. Here is the Day 2 Keynotes video. It begins with Reynold Xin – Chief Architect at Databricks Presenting Real-Time and Spark
It is followed by two other, very good presentations titled – ‘”Leveraging Spark, AWS, And Graph Analytics to Better Protect Customers” and “Data Profiling and Pipeline Processing with Spark – A Journey”’ (58min). Enjoy! Analytics Big Data Real-time Spark
Spark Summit East 2016 took place last week in NYC. Here is the Day 1 Keynotes video. It begins with Matei Zaharia – MIT professor, Databricks co-founder and Creator of Spark – discussing the upcoming release of Spark 2.0.
It is followed by four other, very knowledgeable speakers discussing subjects like ‘Democratizing Spark,’ ‘Enterprise Spark’ and ‘Spark as an Analytics OS.’ (1Hr:12min). Enjoy! Watch Video
Video Lecture | By Prof Zoubin Ghahramani on “Probabilistic Machine Learning – Foundations and Frontiers”
Watch a Video Lecture from the NIPS Conference in December on Probabilistic Machine Learning from one of the greats, Zoubin Ghahramani, Professor of Information Engineering at the University of Cambridge, where he leads the Machine Learning Group. Read More