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Look at this beautiful framework.
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Now we've covered each of these stamps briefly.
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We're going to we're going to dive into each of them one by one.
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The first one is problem definition.
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The question you're trying to answer in the first step is what problem are we trying to solve.
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But before we get into different types of machine learning problems it's important to note machine learning
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isn't the solution to every problem.
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And I think that that's we'd been in a machine learning course but this is this is an important concept
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to remember so when shouldn't you use machine learning.
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Well will a simple hand coded instruction based system work then you should favor the simpler system
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over the machine learning system such as if you wanted to make the favorite chicken dish we used before
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an example if you had the ingredients and you knew the exact steps you had to take to create your favorite
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chicken dish.
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It's probably best that you choose a simple system over using machine learning to try and figure the
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steps out other than these kind of scenarios where you know the simple ham coding instruction based
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system already most of the time you can probably find value using machine learning now comes the first
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step in identifying the problem we're trying to solve as a machine learning problem we can do this by
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matching our problem the one we're working on it might be a business problem or some other kind of problem
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to the main types of machine learning problem.
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These are supervised learning unsupervised learning transfer learning and reinforcement learning.
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We're going to be focused on supervised learning unsupervised learning and transfer learning.
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Why.
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Because this is the most common ones you'll find and you'll come across in practice and then the ones
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when I was Machine Learning engineer when I work on machine learning problems that have proven time
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and time again to be useful supervised learning is called supervised learning because you have data
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and labels a machine learning algorithm tries to use the data to predict a label if it guesses the label
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wrong the algorithm corrects itself and tries again.
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This act of correction is why it's called supervised.
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It's like if you were trying to guess the stamps it took to turn a set of ingredients the data into
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your favorite chicken dish the label.
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If you tried once and got it wrong you'd tell yourself this was wrong.
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Maybe next time we'll try something different.
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A supervised learning algorithm repeats this process over and over and over again trying to get better
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the main types of supervised learning problems a classification and regression classification involves
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predicting if something is one thing or another such as if you wanted to predict whether or not a patient
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had heart disease or not based on their medical records or what type of dog brain was in an image if
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there are only two options.
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It's called binary classification.
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If there are more than two options it's called multi class classification.
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So trying to predict heart disease or not heart disease would be binary classification because there's
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only two classes heart disease or not heart disease and trying to predict different dog breeds based
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on photos in in images would be multi class classification because there are many different kinds of
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dog breeds regression problems involve trying to predict a number you might hear it referred to as a
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continuous number as well which just means a number which can go up or down a classical regression problem
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is trying to predict the sale price of a house based on things like number of rooms the area it's in
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how many bathrooms it has or trying to predict how many people will buy a new app based on Web site
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visits and clicks unsupervised learning has data but no labels.
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For example you might have the purchase history of all customers at your store and your marketing team
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wants to send out a promotion for next summer but they know not everyone will be interested in new summer
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clothes.
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So they come to you as the in-house data science and machine learning engineer and ask Do you know who
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is interested in summer clothes.
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The thing is you don't either but you know you can figure it out from the data you have.
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So you decide to run an algorithm to find patterns in the data and group customers who purchase similar
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things together.
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Once it's finished you notice two groups one group of customers who purchase only during winter time
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and one group of customers who purchase mostly during summertime.
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You label them with winter customers and some customers and send them to your marketing team and they
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thank you for saving them sending out thousands of unwanted emails.
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I'm sure you've probably got some of those kind of emails in your email inbox before what's important
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to note here is that you provided the labels they weren't there to begin with but the patterns were
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and that's what the machine learning algorithm found and after inspecting the groups you're the one
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who saw the commonalities and applied the labels such as summer or winter problems like this are also
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called clustering or putting groups of similar examples together.
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Recommendation problems such as recommending what music someone should listen to based on their previous
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music choices often start out as unsupervised learning problems like this transfer learning leverages
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what one machine learning model has learned in another machine learning for example say you're trying
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to predict what dog breed appears in a photo that's a cute dog.
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That's my that's my poppy 7 and that's Bella in the background.
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She's posing.
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She knows she's she knows she's on this election you could find an existing model which is learned to
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decipher different car types and fine tune it for your task.
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Why is this valuable.
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Because training a machine learning algorithm which means letting it find all of the patterns in data
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can be a very expensive task to find patterns in data.
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Machine learning algorithm has to make millions of calculations.
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And although computers are very fast at making calculations making calculations aren't free.
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So instead of learning everything about different photos from scratch such as what patterns different
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trees look like what different shapes are like the rectangle down here what grass looks like the car
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type model.
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The machine learning model which has figured out what kind of different cars look like has already done
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most of these things if you've already tried to model it might have already figured out okay.
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These are trees not cars these are grass.
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And so it kind of has an idea of what different patterns look like.
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Now you can think of this as being the same as when you write an essay versus writing poetry although
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the writing styles are different.
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The writing that you do uses the same fundamental principles.
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So we can take this car model that identifies different cars and use its foundational patterns and apply
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it to our dog breed problem of course is a few more steps involved here.
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But that's the basic premise of transfer learning reinforcement learning involves having a computer
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program perform some actions within a defined space and rewarding it for doing it well or punishing
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it for doing poorly.
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A good example is teaching a machine learning algorithm to play chess.
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The chess board is a divine space and actions are moving pieces.
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And when I say punishment or reward these things could be as simple as updating a score with plus one
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if it wins a negative one if it loses the machine linings algorithms goal could be to maximize the score.
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So this means if you've done it right it should learn moves which lead to wind reinforcement learning
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is what was used for deep mines Alpha go to become the best go.
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A complicated Chinese ball game far more complicated than chess player of all time defeating many go
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world champions and although promising reinforcement learning has yet to find its way into too many
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practical applications.
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And since we're focused on building practical solutions we've decided to focus on the other kinds of
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learning such as supervised learning unsupervised learning and transfer learning throughout this course.
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Now you know the major types of learning you've now got the tools to tackle step one in the framework
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problem definition aligning the problem you're trying to solve to a machine learning problem.
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So for supervised learning you might say I know my inputs and outputs such as I've got patient records
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could be the inputs and outputs whether or not the patient has heart disease or your inputs could be
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the parameters of a different house and the number of rooms where it's located how many bathrooms there
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are and your outputs are how much the house costs.
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So it's a regression problem and for unsupervised learning you might say I'm not sure of the outputs
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but I do have inputs such as customer purchases and you're trying to figure out which customers are
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most similar to each other or for transfer learning.
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You might think my problem might be similar to something else.
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Can I leverage one existing machine learning model has learned and use it in my own now.
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Don't worry if these kinds of learning are sort of going over your head at the moment we're going to
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be building a hands on project for each of these learning types supervised unsupervised and transfer
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throughout the course.
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In the meantime have a think about some of the problems you face day to day.
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Could any of them be classified as a machine learning problem.
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Are you trying to classify whether one thing is something or another.
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That's a classification problem.
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Do you ever try to predict what a what a number might be that could be a regression problem.
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