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
Watch a free webinar by Mark Meloon, an experienced Data Scientist who has first hand experience with the difficulties landing that first gig as a Data Scientist.
He has helped a lot of others navigate and position themselves to get in the door and start their lucrative careers. It worth listening to what he has to say. His advice is proven to work. He’s given talks a major bootcamps (e.g. Metis, Galvanize) and have made a name for himself advising “aspiring” data scientist.
In this free training will show you his proven step-by-step system for landing the job of your dreams, even if you don’t have prior experience or a Ph.D.!
Free Machine Learning in Python Course | Online Curriculum springboard.com Use this free curriculum to build a strong foundation in Machine Learning, with concise yet rigorous and hands-on Python tutorials https://www.springboard.com/learning-paths/machine-learning-python/ Go!
VIDEO | Join this 6-module session which will take an insightful approach for anyone who wants to get started with deep learning and give intuition to explore the areas of DNN(Deep Neural Networks) April 11th or OnDemand.
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
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 a running time of 1 hour and 16 minutes and covers includes the following […]
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
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