Coming to Cedar Rapids, Iowa May 16-17th, EntreFEST is a two-day conference produced by NewBoCo for professionals to come together and share ideas. ISE's Big Data guru, Matt Coventon, will be returning to the stage for his second year with another riveting session on Machine Learning.
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In my previous blog post I discussed several key takeaways from the 23rd ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD 2017), including time series analysis, survival analysis, and massive online analytics. In this post, I will take a deeper dive into survival analysis. I’ll go into detail on what survival analysis is, how it originated, the components involved, different methods that can be utilized, real-world examples, and open source libraries available.
Two weeks ago I was in Halifax, Nova Scotia attending the 23rd ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD 2017). KDD is one of the largest and most respectable conferences in the data science community. It offers a wealth of knowledge on the latest research in data science, data mining, big data, and predictive analytics. Researchers and professionals come together to learn and discuss novel ideas and technologies to solve challenging problems. My areas of interest lied in time series analysis, IoT streaming, and big data. So here are a few of the interesting things I learned in those areas.
In part 1 of this two part blog post I talked about why you should start using Machine Learning. In particular, I discussed that the barriers to entry in Machine Learning are going down, and although applying it to your business problems may not be easy, it is definitely within reach and can yield great benefits. In part 2, I would like to offer three guiding principles on how to start using Machine Learning.