Machine Learning Throwdown, [Part 1, 2, 3, 4, 5, 6 (complete)]

Machine Learning Throwdown, Part 1 – Introduction by Nick Wilson.

From the post:

Hi, I’m Nick the intern. The fine folks at BigML brought me on board for the summer to drink their coffee, eat their snacks, and compare their service to similar offerings from other companies. I have a fair amount of software engineering experience but limited machine learning skills beyond some introductory classes. Prior to beginning this internship, I had no experience with the services I am going to talk about. Since BigML aims to make machine learning easy for non-experts like myself, I believe I am in a great position to provide feedback on these types of services. But please, take what I say with a grain of salt. I’ll try to stay impartial but it’s not easy when BigML keeps dumping piles of money and BigML credits on my doorstep to ensure a favorable outcome.

From my time at BigML, it has become clear that everyone here is a big believer in the power of machine learning to extract value from data and build intelligent systems. Unfortunately, machine learning has traditionally had a high barrier to entry. The BigML team is working hard to change this; they want anyone to be able to gain valuable insights and predictive power from their data.

It turns out BigML is not the only player in this game. How does it stack up against the competition? This is the first in a series of blog posts where I compare BigML to a few other services offering machine learning capabilities. These services vary in multiple ways including the level of expertise required, the types of models that can be created, and the ease with which they can be integrated into your business.

You need to make decisions on services using your own data and requirements but Nick’s posts make as good a place to start as any.

Will be even more useful if the posts result in counter-posts on other blogs, not so much disputing trivia but in outlining their best approach as opposed to other best approaches.

Could be quite educational.

Series continues with:

Machine Learning Throwdown, Part 2 – Data Preparation

Machine Learning Throwdown, Part 3 – Models

Machine Learning Throwdown, Part 4 – Predictions

Machine Learning Throwdown, Part 5 – Miscellaneous

Machine Learning Throwdown, Part 6 – Summary

Series in now complete.

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