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One thing that I really like doing in my courses is to actually understand the why of everything.
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Everything that we learn there should be a reason we're learning it right.
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And you might be asking yourself a why do we even care about machine learning how is that useful and
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how do we get here.
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Well if you think about a business because most technology evolves from business needs we have the advent
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of computers and the ability for businesses to use computers to do things really really fast and efficiently
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so that they gain an edge.
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And then we got spreadsheets spreadsheets like Excel files and CSP files were amazing because we can
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store data that businesses generate such as maybe customer data into an excel file and then people got
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really really good at analyzing these CSP files these spreadsheets to make business decisions.
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Maybe forecasting that December sales are going to be high because while the past two years we've had
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really high December sales because of Christmas and then as companies got more and more data we started
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getting this idea of the relational databases spreadsheets were great CSP files were great but we started
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getting more and more information and data and we needed a better way to organize things to understand
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things from our data.
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That's when we got things like my askew well which allowed us instead of using spreadsheets to use a
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language called ASCII well to read information from our database right information to our database but
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similar to spreadsheets use the data that we gathered from the business to make business decisions so
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that our business becomes even more profitable.
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And then in 2000 we had this fancy term of big data we had big companies like Facebook Amazon Twitter
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Google that started accumulating more and more data an insane amount of data that you simply couldn't
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contain in a spreadsheet.
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User actions user likes user purchasing histories this idea of big data meant that we had so much data
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these companies had so much data and sometimes unlike relational databases which had to be a structured
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form of data sometimes we got really messy unstructured data and that's where we started getting this
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idea of no Eskew well where things like Mongo D.B. came into existence where you can store unstructured
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data and hopefully make business decisions out of that.
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Maybe if you were Amazon you can use customers purchasing history to recommend different products.
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And ever since then this idea of data getting more and more data has turned us into using machine learning
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because at some point we have so much data that as humans we can't just look like we did at spreadsheets
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and look at columns and rows and make business decisions I mean we still could but then we'd be wasting
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all this data that we've been getting over the years.
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So companies like Facebook and Google that collect massive amounts of data every single day are turning
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to things like machine learning so that instead of humans looking at the data and trying to figure things
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out we give this data to machines so that they're better able even better than humans to make business
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decisions.
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And this idea of machine learning really came to be because of this growth in data that we received
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from businesses as well as the improvements in CPE use GP use that is graphical processing units and
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computer advancements.
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So using the massive amounts of data and massive improvements in computation we can use these machines
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to give them this big data and make a decision for us just like we used to with spreadsheets.
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Now this is a simplified version of how we got here but I hope it gives you a reason as to why businesses
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like this idea of machine learning now in this course we're going to be using this framework and don't
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worry.
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Don't get intimidated.
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You're gonna get really familiar with this framework because well we're going to talk about it a lot
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but looking at this just a brief overview.
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What do you think the hardest part is.
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Can you guess it's this first bar right here.
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Grabbing the data is the amount of data is doubling every two years in our world with the Internet all
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the mobile phones and connected devices.
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We're creating more and more data but this data doesn't mean anything unless we understand it.
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Yes we are producing data but a lot of this data that we generate is unused and that's what data science
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is.
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How can we use this massive quantity of data that is completely useless right now to something that
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is useful and not all data is made equal right.
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Some are noisy some are messy.
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Where do we grab this data from.
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How do we find it.
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How do we clean it so we can actually learn from it.
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We need to understand what data is and then apply machine learning to it.
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And the industry is now evolving into these people that we want to be data scientists that is people
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that can turn data from use less to use for.
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