Learning About Machine Learning: An Introduction
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“Machine learning” is the kind of tech buzzword that is both alluring and vague. From tech giants like Google and Facebook to just-off-the-ground startups, machine learning seems to be everywhere. But what does it actually entail? And what is the best way to get some practical experience with this powerful technology? This post summarizes my introduction to the basics ( very basics ) of machine learning. It also represents my minimum viable product of learning, so to speak, and will hopefully serve as an encouragement to others with little experience that the subject can in fact be approachable. Overview: Supervised Learning with Linear Regression Machine learning is a tool that can give us insight into large datasets. But its real power comes from being able to process data and then make predictions and decisions based on data it has previously processed. Boiled down to its simplest form, when we talk about machine learning we are asking the computer this question, “Given this set of data, what can you tell me about a new data point that you have not yet seen?” In this blog post, I will talk about supervised learning, in which we train the machine to mimic and extend a dataset. What does it mean to mimic and extend a dataset? Let’s look at the simplest example, a line of best fit. If we have a dataset, say data about the relationship between an apartment’s square footage and its rent price, we can plot our data and draw a line that describes the data in the most accurate way. (For the sake of this example, let’s forget about other factors like location.) We can say the best fit line mimics the data because it describes the data trend, but it doesn’t map exactly to the points we plotted...
Where to Begin: Solve a Problem that You Know Well
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In this post, Kerry Sheldon describes her personal project from module 3 of our Back-End program. With this project, Kerry was one of the winners in our first Demo Competition Finals. Our next Demo Competition Finals will be held on Thursday, October 6th. Stay tuned for a meet-up announcement! I enrolled at the Turing School of Software and Design because my “ideas" notebook in Evernote had become a virtual graveyard. I couldn’t bear to open it; the gulf between my skills and ambitions was too large. In the five months I’ve been at Turing, I flexed a lot of muscles that I hadn’t used in a long time. But one remained relatively dormant. My “idea" muscles were atrophying. At the end of Turing’s third module (the program consists of four 6-week modules), students work on a self-directed individual project. I wanted to build something with immediate utility. I needed an idea that didn’t require a client or organizational owner, or depend on a network of users in order to be useful. I wasn’t ready to face the Evernote notebook, but I could solve a problem that I’d been having. I built CodePoints as a productivity app for beginning programmers that allows users to set small weekly practice goals for focused programming skills they want to develop. Users log their practice sessions from the web app or from a companion command line app. The app tracks practice activities by skill, has a point system that rewards goal achievement, and provides data on practice sessions for a variety of time periods. User’s current week dashboard of goals and logged practice sessions Before I came to Turing, I made a few attempts to teach myself to program. I was awed and excited by the large and growing number of free (or affordable) resources...
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