Business and predictive analytics are the pinnacle of a mature data lifecycle, and Machine Learning is an exceptional tool providing in-depth statistical analysis of data. One of the common misconceptions about Machine Learning is the level of mathematical experience required, and Pragmatic Works' Business Intelligence Consultant and Data Aficionado Ginger Grant is on a mission to set the record straight. In her upcoming webinar, Complex Data Analysis and Azure Machine Learning, she'll be explaining how to leverage Azure ML to analyze cloud data without having to know a lot of complex formulas.
I had an interesting conversation with someone at SQL Saturday Phoenix, an event that I am happy I was able to attend, regarding knowing math and getting started in Machine Learning (ML). As someone who had majored in math in college, he was sure that you had to know a lot of math to do ML. While I know having really good math skills can always be helpful when creating statistical models based on probability - a big part of Machine Learning - I do not believe that you need to know a lot of math to do Azure Machine Learning.
Azure Machine Learning and Throwing Spaghetti Against the Wall
For those of you who cook, you may have heard of an old school way of testing to see if the spaghetti is done. You throw the spaghetti against the wall and if it sticks, the pasta is done. If it falls right off, keep the spaghetti in the pot for a while longer. Testing machine learning models is similar, but instead of throwing the computer against the wall, you keep on testing using the large number of models available in Azure ML. Once you have determined the classification of your data, there are a number of different models for the classification which you can try without knowing all of the statistical formulas behind each model. I have listed all of the models from Azure ML to the right so that you can take a look at the large number of models available.
By taking a representative sample of your data and testing all of the related models, determining which one will provide a result is not terribly difficult. The reason it is not very hard is you do not have to understand the underlying math needed to run the model. Instead you need to learn how to read a ROC curve, which I included in my last blog post. While you can pick the appropriate model by having a deep understanding of the formula behind each model, you can achieve similar results by running all of the models and selecting the model based on the data.
Advanced Statistical Analysis and Azure ML
While Azure ML contains a lot of good tools to get started if you do not have a data scientist background, which recruiters lament not enough people do, why would you use Azure ML if you have coded a bunch of R Modules already to analyze your data? Because you can use Azure ML to call those modules as well and provides a framework to raise visibility and share those modules with people within your organization or the world, if you prefer.
How to Pick the Right Model
I am going to demonstrate how to pick the right model in an upcoming webinar, which is probably easier to explain in that fashion rather than in a blog post. If you want to see how to determine which model to use and not know a lot of math, this webinar will be very helpful.
Azure ML offers the ability to integrate analysis into your data environment without having to be a data scientist, while providing advanced features to accommodate those really good at math, which I will be talking about in an upcoming Preconvention event for SQL Saturday in Huntington Beach. If you happen to be in Southern California on April 10th, I hope you will be able to attend that event.
Ginger's upcoming webinar, Complex Data Analysis and Azure Machine Learning, will be presented on March 17th at 11 a.m. ET.
Looking for more in-depth info on how to use the very cool Azure ML tools and get a grip on the science behind Machine Learning? Check out this end-to-end, demo heavy, data science case study…from raw data to prediction.