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I welcome to chapter 6.1 which is basically a machine learning crash course overview.

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All right.

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So let's get started into this so what is machine learning now.

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Machine learning has been synonymous to artificial intelligence because basically it's a field of study

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that basically studies how algorithms or software actually learns from data.

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So basically as I said some field or field in artificial intelligence that uses statistical techniques

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to give computers the ability to learn from data without being explicitly programmed and explicitly

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means like if this is dad and that is that basically a hard look up table of criteria machine learning

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does not do that.

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It learns from the data learns it's one model of how it should be answered and basically over the last

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five to 10 years maybe even 15 years or so machine learning has exploded there.

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Basically a number of masters and BSD programs all over the world have specializations in machine learning

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now it is and this has mainly been brought brought born because processing power and also from GPS use

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has basically caught up to the process of intensive intensity required for machine learning.

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So there are four types of machine learning with neural networks being basically one type belonging

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to us.

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A subtype belonging to one type of DS for these four are basically supervised unsupervised self supervised

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and reinforcement learning.

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And I'm going to talk a little bit about each one so fiercely supervised living now supervised living

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is by far the most popular form of E.I. and well used today.

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And they'll being machine learning basically because it's relatively easy compared to other things to

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implement.

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All they need is labelled data set and we feed this data set into machine learning model algorithm and

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it develops a model to fit this data to some outputs.

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So basically it's like an example here is let's see if we have 10000 emails that are labeled spam 10000

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that are not spam and we give this to a model.

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Basically we get the text and Miss riskless subjects and from the sender of the email and now the e-mail

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story the machine learning algorithm is now going to figure out what is spam based on that.

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So we have input data being an e-mail model that we just trained and it outputs but it's or not.

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So that in a nutshell is supervised learning.

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And here are some examples of supervised living in crappy division.

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Basically it's used heavily in image justification even object detection and segmentation.

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Basically all of these involve feeding some label data into our depleting model and treating it and

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getting a model that is accurate enough to take unseen data and classified them correctly.

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So what about unsupervised learning now unsupervised learning learning is concerned with finding interesting

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clusters Indian data and it does so without any hope of data labeling.

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So you just feed some data into it and the unsupervised learning algorithm basically finds interesting

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patterns and clusters in the information

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it is actually very important in data analytics when you're trying to understand vast amounts of data

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of that data.

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Basically when you have huge data sets with huge number of columns and rows and different information

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using unsupervised learning can help you understand very quickly what is important in your data.

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This is a best example of it here.

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Go back to it.

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So now imagine we have basically meats of food items here.

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And we give it as a seamstress pictures here and we'll give it to an unsupervised machine learning algorithm

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and it's going to actually pick it close to what interests you and I'm willing to bet close to what

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is going to be the first tree I trusted to baby does it's a meets it's that is interesting pattern unsupervised

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machine learning algorithms help us pick out.

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So what about self supervised learning.

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Now sylphs revised learning is the same concept as supervised learning.

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However that data is not labeled by humans which is pretty interesting so how it is level is generated.

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Basically it's done using heuristic algorithms.

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Example also includes an A good example of that is basically trying to predict the next Freman a video

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given the previous frames.

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That's a very good example of self supervised learning.

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We're not going to deal with any self-sacrifice or unsupervised learning in this class but it's good

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to know.

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If you want to have an overview of machine learning what they're about.

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And lastly we have reinforcement learning that reinforcement learning is potentially very interesting.

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However it's still in its infancy and it's still a bit tricky to actually do.

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I actually did a couple of courses classes on this in my university in Edinburgh and it wasn't that

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fun.

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Was actually very challenging but once I got it working it was actually quite fun quite cool.

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So the concept is pretty simple.

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However it's that simple to implement.

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But we basically teach the algorithm something by giving it bad examples or penalties against something.

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So it's of like learning to play games.

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It's a very good example of reinforcement learning.

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You're basically trying different things and getting punished or dying or losing points for something

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until you come up with a strategy where you're basically minimizing your loss.

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That is what reinforcement learning technically is.

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So in machine learning it and machining in supervised machine learning is a basic tenet process which

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you follow in every basically every using of every algorithm whether it be deplaning convolutional and

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that's SVM.

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Blah blah blah.

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They all follow this pattern.

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So in step one you obtain a label dataset.

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Step two is split to say the set.

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This is very important into a trining portion and the validation or test portion noted is technically

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a little difference between the validation and test portion.

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However for all intents and purposes which is about Free's I shouldn't be using but whatever.

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Basically validation and test push is basically the unseen data.

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Your model never sees this data model only sees the training data and we test performance on the validation

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or test push and test tested assets.

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So this in step 3.

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We take this training data set that we split from the original.

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And we feel it's our model.

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So model takes us still and isn't inputs all labels and basically loon's tries to figure out patterns

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how do we predict this.

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How do we know what this is after some time it develops a fully trained model and so forth.

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Basically we run this model in our test validation dataset to see how effective it is.

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So here's some machine learning terminology that you're probably going to hear in this course and it

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will basically target data with all this if we is to the ground troop levels technically in programming

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languages.

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You'll see that refer to as the y o In mathematics the Y labels X being the training data set.

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Sorry about that.

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And prediction basically being what all models predicted from some input data classes will be basically

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the categories of your data.

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So if you were talking about the hand-written digit amnesty the set there were 10 classes there would

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be zero 1 2 3 4 5 6 7 8 9 aggression or phrase do when you're in classes we are operating basically.

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This image belongs to this last regression.

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We're putting basically a continuous value digit number.

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So that's say we taking some inputs and trying to predict someone's height or weight that would be a

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regression model.

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And as I mentioned before invalidations last tests yes to can be different.

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But in early Christou unseen data that we test our tree and model on.