A Brief History of Machine Learning

Machine learning is a data science technique that allows computers to use existing data to forecast future behaviors, outcomes, and trends. Using machine learning, computers learn without being explicitly programmed.

Azure Machine Learning is a cloud predictive analytics service that makes it possible to quickly create and deploy predictive models as analytics solutions.

Join Ryan Berry and Matthew Puckett as they welcome Microsoft Data Scientist extraordinaire Laura Edell as they provide a brief history of Machine Learning and how you can apply it in your daily operations.

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0:03:29 – What is Machine Learning and what does it do exactly?
0:05:24 – What are the 3 different types of Machine Learning?
0:12:59 – Where should I begin when first building a machine learning model?
0:16:10 – How would I begin to build a model? What’s involved?
0:23:01 – Why do we split our data into a training set vs. test set?
0:27:38 – How important is setting a baseline before building your data model?
0:30:41 – How do we properly evaluate a model? How can we say what is good vs. what is bad?
0:37:52 – Let’s talk about decision trees – how valuable are these to setting up you data model?
0:40:53 – What’s a “Recommendation System” --- how can I set this up and what variations are there?
0:47:45 --- Ok let’s talk about the important stuff --- how can Machine Learning help my Fantasy Football team? 
0:52:01 – How does Azure Machine Learning Services fit in here?

[MarketingWill]

This is such a great overview of machine learning. Great work guys. I would love to see Laura back on for another, more in-depth machine learning example!