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All right, so you want to understand machine learning algorithms.

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Yeah, it's a pretty, pretty fascinating field.

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It is. And luckily, I think this educational overview is going to be a really great place to start.

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For sure. For sure.

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By the end of this deep dive, I think you'll not only be able to choose the right algorithm,

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but really get like the intuition behind each one.

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So you're not just kind of lost in the jargon.

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Exactly. And what I think is so cool about it is that you're basically teaching computers

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to learn without, you know, having to tell them every single step of the way.

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Yeah. So let's start with the basics.

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What is machine learning according to this overview?

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Well, it defines it as, you know, a subset of AI that deals with these algorithms that can learn from data.

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And the key here is that they can then apply that knowledge to new data that they've never seen before.

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So it's not just about like memorizing the data that they've seen, but it's about actually learning from it.

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Exactly. Generally.

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Or make predictions. Yeah. It's huge.

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Yeah. It's like giving them the ability to kind of see into the unknown.

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It really is. And, you know, think about all the advancements we've seen recently, especially with neural networks.

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You know, those are behind everything from, you know, image recognition to language translation.

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It's incredible. And it's all because they can kind of learn these really complex patterns from massive amounts of data.

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Right.

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But let's not get ahead of ourselves.

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Yeah. Yeah.

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This overview talks about two main types of machine learning.

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Right.

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Supervised and unsupervised.

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So what's the difference between the two?

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So supervised learning is probably what you're most familiar with.

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Okay.

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And it's kind of like having a teacher, you know, you're giving the algorithm labeled data, so you already know the answer.

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And it's learning from those examples to make predictions on new data.

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So it's kind of like giving it a set of flashcards.

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Exactly.

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With a question on one side and the answer on the other.

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And it can study the flashcards and then try and answer new questions based on what it's learned.

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Yeah. Like let's say you're trying to predict house prices.

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So you would give the algorithm data like the square footage, the location, the number of bedrooms,

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and of course the selling price of similar houses.

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And it figures out how those features relate to the price and then uses that knowledge to predict a new house price.

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Based on its characteristics.

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Exactly.

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Yeah. Very cool.

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So it's basically learning from past data to make predictions about the future.

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And what about unsupervised learning?

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Is that more of a free-for-all?

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Yeah, you could say that.

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Okay.

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So in unsupervised learning, we don't give the algorithm labeled data.

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We don't give it any predefined categories.

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We just let it loose on a data set and it has to figure out the patterns and the structures all on its own.

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It's like giving someone a jigsaw puzzle without the picture on the box and saying good luck.

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Right. Or, you know, imagine you gave a child a bunch of different animal pictures,

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but you didn't tell them what each animal was.

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They could probably still group them together based on, you know...

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Yeah, what they look like.

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What they look like, right?

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They might put all the furry, four-legged creatures together, the birds together.

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Right.

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That's kind of how unsupervised learning works.

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So it's more about discovering hidden patterns and relationships in the data.

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Exactly. Yeah.

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I see. So I think since supervised learning is kind of like the starting point for a lot of people in machine learning.

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Right.

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This overview mentions linear regression as one of the simplest algorithms.

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Yes, it is.

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And a good place to begin.

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A great place to begin.

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So let's unpack linear regression. What is it?

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So at its core, linear regression is trying to find a linear relationship between an input variable and an output variable.

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Okay.

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So if you think about it visually, it's like drawing a straight line through a scatter plot of data points.

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That line represents the relationship between the variables and we can use it to make predictions.

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So if I was trying to predict how much money I'd make selling lemonade based on the temperature outside,

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I could use linear regression to kind of find that relationship between temperature and sales.

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So you could collect data on how much lemonade you sell at different temperatures

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and use linear regression to find the line that best fits that data.

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And if the line slopes upward, it would mean that as the temperature increases,

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your lemonade sales tend to increase as well.

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That makes sense. But what if the relationship isn't that straightforward?

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What if there are other factors at play?

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Well, linear regression assumes a direct proportional relationship between those variables.

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Okay.

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So it's great for things like predicting sales based on advertising spending

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or maybe estimating the lifespan of a machine based on usage.

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Okay.

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But if the relationship's more complex, you'd have to explore other algorithms.

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So linear regression is a powerful tool, but not a one size fits all solution.

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Exactly. And that's why it's so important to understand these different types of algorithms and when to use them.

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For sure.

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All right. So let's move on to another supervised learning algorithm

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that's often used for classification tasks.

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Oh.

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Logistic regression.

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So it's like linear regression, but for classifying things into categories.

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That's the idea.

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Instead of predicting like a specific number.

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So while linear regression predicts a continuous output.

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Okay.

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Logistic regression predicts the probability of something belonging to a particular category.

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Okay. Can you give me an example?

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Yeah. Let's say you want to predict whether a customer will click on an ad based on their demographics or browsing history.

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Okay.

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You could use logistic regression to model that the algorithm would learn the relationship between those features

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and the likelihood of clicking on the ad.

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Okay.

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Giving you a probability score for each customer.

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So it's basically like predicting whether they'll say yes or no to clicking on the ad.

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Exactly. And based on those probabilities, you could target your ads more effectively

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to reach the customers most likely to be interested.

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That's really cool.

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So I know there are a lot more supervised learning algorithms out there.

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What's next on our list?

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So let's talk about K nearest neighbors.

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Often called K and M.

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Okay.

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It's a very intuitive algorithm.

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It's easy to understand.

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K nearest neighbors.

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I like that.

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How does that work?

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Yeah.

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So imagine you have a scatter plot of data points.

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Okay.

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And each one belongs to a specific category.

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So to classify a new data point.

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Yeah.

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K and N looks at its K nearest neighbors.

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Okay.

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So the K data points that are closest to it in terms of their features.

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And if most of those neighbors belong to a certain category,

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K and N predicts that the new data point also belongs to that category.

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So it's kind of like judging a book by its cover or I guess judging a data point by the company it keeps.

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That's a great way to put it.

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Yeah.

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Think of it like this.

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You're at a party.

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Okay.

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Most of the people standing near you are wearing football jerseys.

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Yeah.

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You might assume you're at a Super Bowl party.

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Right.

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K and N works similarly.

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That makes sense.

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So if I'm trying to classify a new customer as, you know,

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a high-spender or a low-spender based on their purchase history.

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Right.

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K and N would look at the customers most similar to them and their spending habits.

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Exactly.

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And make a prediction.

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It's a simple but powerful algorithm that can be surprisingly effective.

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This is great.

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We're really building up our understanding of these different classification approaches.

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Are there any other supervised learning algorithms we should touch on?

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Absolutely.

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One that's particularly well known and powerful is support vector machines or SVMs.

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Okay.

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SVMs, they sound kind of intense.

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What makes them so special?

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So SVMs are like the strategists of the machine learning world.

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Okay.

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They're all about finding the best boundary to separate different categories of data.

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So if we're back to that scatter plot example, they would try to draw a line.

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Precisely.

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That neatly divides those different categories.

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Exactly.

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But they don't just draw any line.

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They try to find the line that maximizes the margin between the categories,

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meaning that empty space between the line and the closest data points.

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Gotcha.

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So why is that maximizing the margin so important?

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Well, think about it.

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If your dividing line is right up against some data points, any slight variation could push them to the wrong side.

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A wider margin just makes the model more robust.

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That's like giving yourself some breathing room.

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Yeah, exactly.

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To avoid misclassifications.

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Exactly.

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Okay.

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Can you give us an example of how SVMs might be used?

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Yeah.

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Let's say you're building a system to detect spam emails.

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Okay.

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An SVM could analyze the words and phrases and emails and find the best boundary to separate spam from load-in-met messages.

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Interesting.

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And the data points closest to the margin.

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Yeah.

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We call those support vectors.

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Support vectors.

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Those are the ones that are really important in determining where the line is drawn.

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They're like the boundary guards.

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Yes, exactly.

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Of the SVM world.

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They are.

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So what else makes SVM so powerful?

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One of the things that sets them apart is their ability to handle high-dimensional data.

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So data with lots of features.

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Okay.

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And they can also use something called kernel functions.

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Okay.

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To create complex nonlinear decision boundaries.

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Kernel functions.

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Those sound pretty complicated.

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They can be, but think of it like this.

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Sometimes it's easier to separate two groups of objects if you kind of move them into a different space.

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Imagine trying to separate a mixture of marbles and ping pong balls.

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Okay.

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Yeah.

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It might be tricky to do on a flat surface, but if you pour them into a container with different sized holes,

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they'll naturally separate.

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They'll fall through.

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Yeah.

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Kernel functions allow SVMs to do something similar.

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That's a really cool analogy.

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So I'm starting to see why SVMs are considered so versatile.

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Yeah.

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Are there any other classification algorithms we should know about?

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Yeah, there are many more, but let's touch upon one more that's often used in things like text classification or spam filtering.

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Okay.

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The naive Bayes classifier.

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Naive Bayes?

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Is it naive because it's overly simplistic or?

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Well, the naive part comes from the assumption it makes about the data.

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Okay.

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It assumes that the features are independent of each other, which isn't always true in real-world situations.

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Right.

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But despite the simplification, naive Bayes can be surprisingly effective, especially for text-based tasks.

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So it's naive in the sense that it kind of makes a simplifying assumption.

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Exactly.

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But how does it actually work?

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So imagine you're building a spam filter.

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Naive Bayes would look at the words in an email and calculate the probability of each word appearing in a spam email versus a non-spam email.

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Gotcha.

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And then using a mathematical formula called Bayes theorem, it combines those probabilities to fill the space.

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And it's also a way to figure out the probabilities to figure out the likelihood that the email is spammed.

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So it's like creating a dictionary of spammy words and seeing how many of them appear in a new email.

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Yeah.

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And if the email's riddled with spammy words, it's more likely to get flagged.

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Gotcha.

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That makes sense.

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So we've covered linear regression, logistic regression, k-nearest neighbors, SVMs, and naive Bayes.

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We have.

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What other supervised learning algorithms should we add to our toolbox?

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We can add a new algorithm for some even more powerful techniques.

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Decision trees.

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Decision trees.

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All right.

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I'm guessing that involves making decisions.

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It does think of it like a flow chart.

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OK.

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Where you ask a series of yes-no questions to arrive at a decision.

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That's what a decision tree does.

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It divides the data based on different features, creating a Cree-like structure where each branch represents a decision.

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I can picture that.

254
00:11:12,000 --> 00:11:14,320
So it's like playing a game of 20 questions.

255
00:11:14,320 --> 00:11:14,960
Yes.

256
00:11:14,960 --> 00:11:18,640
Where the algorithm is asking questions about the data to try and reach a conclusion.

257
00:11:18,640 --> 00:11:19,360
Exactly.

258
00:11:19,360 --> 00:11:19,760
Yeah.

259
00:11:19,760 --> 00:11:23,760
Imagine you're creating a system to assess the risk of a heart attack.

260
00:11:23,760 --> 00:11:28,240
You have data on people's age, blood pressure, cholesterol levels.

261
00:11:28,240 --> 00:11:33,360
Whether or not they smoke the decision tree might start by asking, is the person over 50?

262
00:11:33,360 --> 00:11:33,840
OK.

263
00:11:33,840 --> 00:11:35,680
If yes, it goes down one branch.

264
00:11:35,680 --> 00:11:37,280
If no, it goes down another.

265
00:11:37,280 --> 00:11:37,840
Right.

266
00:11:37,840 --> 00:11:40,560
Each branch would ask further questions.

267
00:11:40,560 --> 00:11:41,200
OK.

268
00:11:41,200 --> 00:11:44,720
Leading to a final classification of high risk or low risk.

269
00:11:44,720 --> 00:11:49,680
So the goal is to find the best questions to ask in order to separate the data effectively.

270
00:11:49,680 --> 00:11:50,160
You got it.

271
00:11:50,160 --> 00:11:52,160
It's like the algorithm is playing detective.

272
00:11:52,160 --> 00:11:52,320
Right.

273
00:11:52,320 --> 00:11:53,280
Trying to crack the case.

274
00:11:53,280 --> 00:11:53,520
Yeah.

275
00:11:53,520 --> 00:11:55,360
Trying to figure out who's at risk, who isn't.

276
00:11:55,360 --> 00:11:55,920
Exactly.

277
00:11:55,920 --> 00:12:03,040
And so while a single decision tree can be helpful, I'm guessing things get even more interesting when you combine multiple decision trees.

278
00:12:03,040 --> 00:12:03,920
They do.

279
00:12:03,920 --> 00:12:07,360
That brings us to the world of ensemble algorithms.

280
00:12:07,360 --> 00:12:08,560
Ensemble algorithms.

281
00:12:08,560 --> 00:12:08,800
OK.

282
00:12:08,800 --> 00:12:11,760
So that's like a team of decision trees working together.

283
00:12:11,760 --> 00:12:12,240
Yeah.

284
00:12:12,240 --> 00:12:17,680
You could say that ensemble algorithms combine multiple models to create a more powerful and robust predictor.

285
00:12:17,680 --> 00:12:18,160
Yeah, sure.

286
00:12:18,160 --> 00:12:22,320
And one popular method is called bagging short for bootstrap aggregating.

287
00:12:22,320 --> 00:12:23,520
Bootstrap aggregating.

288
00:12:23,520 --> 00:12:25,200
That sounds very self-sufficient.

289
00:12:25,200 --> 00:12:25,680
It is.

290
00:12:25,680 --> 00:12:28,480
So imagine you have a big bag of data.

291
00:12:28,480 --> 00:12:28,880
OK.

292
00:12:28,880 --> 00:12:35,040
Bagging creates multiple smaller bags by randomly sampling data points from the original bag.

293
00:12:35,040 --> 00:12:38,160
Then it trains a separate model on each of these smaller bags.

294
00:12:38,160 --> 00:12:38,560
OK.

295
00:12:38,560 --> 00:12:43,200
And finally, it combines the predictions from all those models to make a final prediction.

296
00:12:43,200 --> 00:12:47,440
So it's like creating multiple mini experts and having them vote on the final answer.

297
00:12:47,440 --> 00:12:47,840
Exactly.

298
00:12:47,840 --> 00:12:50,160
It's like crowdsourcing the decision-making process.

299
00:12:50,160 --> 00:12:50,400
OK.

300
00:12:50,400 --> 00:12:51,280
Like that.

301
00:12:51,280 --> 00:12:54,720
A famous example is the random forest algorithm.

302
00:12:54,720 --> 00:12:55,440
Random forest.

303
00:12:55,440 --> 00:12:58,480
So it's full of randomly generated decision trees.

304
00:12:58,480 --> 00:13:05,680
You could say that a random forest combines many decision trees, each trained on a different random subset of the data.

305
00:13:05,680 --> 00:13:06,320
OK.

306
00:13:06,320 --> 00:13:09,840
And this randomness helps prevent something called overfitting.

307
00:13:09,840 --> 00:13:10,560
Overfitting.

308
00:13:10,560 --> 00:13:14,080
Which is when the model learns the training data too well.

309
00:13:14,080 --> 00:13:14,320
OK.

310
00:13:14,320 --> 00:13:16,640
And then doesn't perform as well on new data.

311
00:13:17,360 --> 00:13:24,000
So it's like planting a forest of decision trees with different perspectives and then letting them all have a say in the final prediction.

312
00:13:24,000 --> 00:13:25,040
That's a great analogy.

313
00:13:25,040 --> 00:13:30,720
And because each tree is trained on a different subset of the data, the overall model is more robust.

314
00:13:30,720 --> 00:13:31,040
Right.

315
00:13:31,040 --> 00:13:36,080
It's like having a diverse team of experts all working together to solve a problem.

316
00:13:36,080 --> 00:13:36,640
Yeah.

317
00:13:36,640 --> 00:13:40,800
So what other types of ensemble algorithms are out there?

318
00:13:40,800 --> 00:13:43,360
Another important one is called boosting.

319
00:13:43,360 --> 00:13:43,840
Boosting.

320
00:13:43,840 --> 00:13:46,800
So unlike bagging, which trains models in parallel.

321
00:13:46,800 --> 00:13:47,440
Yeah.

322
00:13:47,440 --> 00:13:50,080
Boosting trains models sequentially.

323
00:13:50,080 --> 00:13:54,560
Each new model is focusing on fixing the mistakes made by the previous models.

324
00:13:54,560 --> 00:14:00,240
So it's like a chain of experts where each one specializes in correcting the errors of the one before them.

325
00:14:00,240 --> 00:14:04,400
Exactly like a relay race where the batons pass from one model to the next.

326
00:14:04,400 --> 00:14:06,320
That's a really good way to visualize it.

327
00:14:06,320 --> 00:14:06,720
Yeah.

328
00:14:06,720 --> 00:14:09,760
This sequential approach can lead to higher accuracy.

329
00:14:09,760 --> 00:14:10,160
OK.

330
00:14:10,160 --> 00:14:14,800
But it can also be prone to overfitting if it's not carefully tuned.

331
00:14:14,800 --> 00:14:15,120
Got it.

332
00:14:15,120 --> 00:14:17,920
So bagging is about combining diverse perspectives.

333
00:14:18,480 --> 00:14:21,760
Boosting is about refining the model step by step.

334
00:14:22,480 --> 00:14:25,360
What are some specific boosting algorithms?

335
00:14:26,000 --> 00:14:30,640
So some popular ones include Atta Boost, Gradient Boosting and XGBoost.

336
00:14:30,640 --> 00:14:35,680
They all use different techniques to improve the model sequentially, but the core idea remains the same.

337
00:14:35,680 --> 00:14:36,240
Gotcha.

338
00:14:36,240 --> 00:14:40,320
This has been an amazing journey through supervised learning algorithms.

339
00:14:40,320 --> 00:14:41,200
It has.

340
00:14:41,200 --> 00:14:48,160
From the simplicity of linear regression to these really powerful ensemble methods.

341
00:14:48,800 --> 00:14:53,600
It's incredible to see how these algorithms can learn from data and make predictions.

342
00:14:53,600 --> 00:14:54,320
It is.

343
00:14:54,320 --> 00:14:55,600
But I'm curious about neural networks.

344
00:14:56,160 --> 00:14:58,000
They seem to be all the rage these days.

345
00:14:58,000 --> 00:14:58,400
They are.

346
00:14:58,400 --> 00:14:59,920
What makes them so special?

347
00:14:59,920 --> 00:15:03,520
Well, neural networks have definitely taken center stage in the AI world.

348
00:15:03,520 --> 00:15:08,800
And they're a powerful type of machine learning algorithm that's inspired by the structure of the human brain.

349
00:15:09,360 --> 00:15:11,200
So they're basically artificial brains.

350
00:15:11,200 --> 00:15:13,280
Well, it's not quite that simple.

351
00:15:13,280 --> 00:15:13,760
OK.

352
00:15:13,760 --> 00:15:15,200
But the analogy is helpful.

353
00:15:15,200 --> 00:15:20,480
So neural networks are made up of interconnected nodes or neurons organized in layers.

354
00:15:20,480 --> 00:15:28,320
And these neurons process and transmit information, allowing the network to learn complex relationships in the data.

355
00:15:28,320 --> 00:15:28,800
OK.

356
00:15:28,800 --> 00:15:29,440
I'm intrigued.

357
00:15:29,440 --> 00:15:30,880
Can you walk me through how they work?

358
00:15:31,680 --> 00:15:36,560
So to understand neural networks, let's revisit logistic regression for a moment.

359
00:15:36,560 --> 00:15:36,960
OK.

360
00:15:36,960 --> 00:15:41,840
Remember how we talked about it predicting the probability of something belonging to a category?

361
00:15:41,840 --> 00:15:44,640
Yeah, like predicting whether a customer will click on an ad.

362
00:15:44,640 --> 00:15:45,200
Exactly.

363
00:15:45,200 --> 00:15:48,480
Logistic regression is great for simple relationships.

364
00:15:48,480 --> 00:15:49,040
OK.

365
00:15:49,040 --> 00:15:52,000
But it struggles with more complex patterns.

366
00:15:52,000 --> 00:15:55,680
So think about trying to recognize handwritten digits.

367
00:15:55,680 --> 00:16:04,400
Yeah. There's so much variation in how people write that a simple linear model would have a really hard time capturing all those nuances.

368
00:16:04,400 --> 00:16:05,520
You would get confused.

369
00:16:05,520 --> 00:16:05,840
Yeah.

370
00:16:05,840 --> 00:16:07,840
And this is where neural networks excel.

371
00:16:07,840 --> 00:16:11,840
So neural networks are better at handling messier real world data.

372
00:16:11,840 --> 00:16:17,600
You could say that one of the key differences is that neural networks can learn features from the data automatically.

373
00:16:17,600 --> 00:16:18,080
Oh, yeah.

374
00:16:18,080 --> 00:16:22,640
They don't need us to explicitly define the features like we did with logistic regression.

375
00:16:22,640 --> 00:16:23,760
So they're kind of independent.

376
00:16:23,760 --> 00:16:26,720
They are. So let's stick with that handwritten digit example.

377
00:16:26,720 --> 00:16:27,360
OK.

378
00:16:27,360 --> 00:16:36,480
A neural network with hidden layers might learn to recognize features like horizontal lines, vertical lines, curves and loops in the digits.

379
00:16:36,480 --> 00:16:42,800
And these features, which we might not even be aware of, then help the network classify the digit correctly.

380
00:16:42,800 --> 00:16:47,280
So the hidden layers are like the network's own internal feature engineers.

381
00:16:47,280 --> 00:16:48,720
Yeah. A great way to put it.

382
00:16:48,720 --> 00:16:52,480
They're working behind the scenes to try and find the best way to represent the data.

383
00:16:52,480 --> 00:16:59,040
Exactly. And the more hidden layers the network has, the more complex the features it can learn.

384
00:16:59,040 --> 00:16:59,520
OK.

385
00:16:59,520 --> 00:17:01,760
And that's the essence of deep learning.

386
00:17:01,760 --> 00:17:08,640
Deep learning. So it's like having this team of super sleuths all working together to try and crack the case of what the data means.

387
00:17:08,640 --> 00:17:17,040
Exactly. And deep learning has revolutionized so many fields from image recognition to natural language processing,

388
00:17:17,040 --> 00:17:21,120
it's powering self-driving cars and voice assistance and all sorts of things.

389
00:17:21,120 --> 00:17:27,040
It's incredible. It sounds like neural networks, especially deep learning, are kind of pushing the boundaries of what's possible with AI.

390
00:17:27,040 --> 00:17:30,320
This has been a fantastic exploration of supervised learning.

391
00:17:30,320 --> 00:17:31,200
It has.

392
00:17:31,200 --> 00:17:34,400
But this overview also mentions unsupervised learning.

393
00:17:34,400 --> 00:17:36,480
Are you ready to kind of delve into that realm?

394
00:17:36,480 --> 00:17:39,440
Absolutely. Unsupervised learning is where things get really interesting.

395
00:17:39,440 --> 00:17:42,800
You know, it's like exploring uncharted territory.

396
00:17:42,800 --> 00:17:43,440
I like that.

397
00:17:43,440 --> 00:17:46,160
Discovering hidden treasures in the data.

398
00:17:46,160 --> 00:17:47,760
I'm ready for treasure hunt.

399
00:17:47,760 --> 00:17:48,560
Let's go.

400
00:17:48,560 --> 00:17:52,640
OK. So where do we start our treasure hunt in unsupervised learning?

401
00:17:52,640 --> 00:17:53,920
Let's start with clustering.

402
00:17:53,920 --> 00:17:59,200
Clustering. So is that like sorting your laundry in different piles like socks, shirts, towels?

403
00:17:59,200 --> 00:18:00,400
That's a pretty good analogy.

404
00:18:00,400 --> 00:18:01,200
OK.

405
00:18:01,200 --> 00:18:07,280
So in clustering, we're trying to find groups or clusters of similar data points within a data set.

406
00:18:07,280 --> 00:18:08,080
OK.

407
00:18:08,080 --> 00:18:12,640
The key difference from supervised learning is that we don't know what those groups are beforehand.

408
00:18:13,200 --> 00:18:14,240
I don't have those labels.

409
00:18:14,240 --> 00:18:15,840
We don't have the labels.

410
00:18:15,840 --> 00:18:18,400
There are no predefined categories to guide us.

411
00:18:18,400 --> 00:18:21,840
So it's like giving someone a box of Legos and saying, figure out what you can build.

412
00:18:21,840 --> 00:18:22,320
Exactly.

413
00:18:22,320 --> 00:18:25,040
Or imagine looking at a scatter plot of beta points.

414
00:18:25,040 --> 00:18:27,760
But this time, you don't know anything about their categories.

415
00:18:27,760 --> 00:18:28,240
Right.

416
00:18:28,240 --> 00:18:32,560
A clustering algorithm would try to group those points based on how similar they are,

417
00:18:32,560 --> 00:18:36,960
maybe by how close they are to each other, or some other measure of resemblance.

418
00:18:36,960 --> 00:18:40,320
OK. So it's about finding those natural groupings in the data,

419
00:18:40,320 --> 00:18:43,120
the ones that make the most sense based on their features.

420
00:18:43,120 --> 00:18:45,360
So how do these clustering algorithms work?

421
00:18:45,360 --> 00:18:49,760
Well, one of the most popular algorithms is called K-means clustering.

422
00:18:49,760 --> 00:18:50,720
K-means.

423
00:18:50,720 --> 00:18:53,040
Does the K stand for the number of clusters we're looking for?

424
00:18:53,040 --> 00:18:53,520
Yes.

425
00:18:53,520 --> 00:18:54,000
You got it.

426
00:18:54,000 --> 00:18:54,560
OK.

427
00:18:54,560 --> 00:18:57,200
So we tell the algorithm how many clusters we want.

428
00:18:57,200 --> 00:18:58,000
That's our K.

429
00:18:58,000 --> 00:19:00,320
And it tries to group the data accordingly.

430
00:19:00,320 --> 00:19:00,800
OK.

431
00:19:00,800 --> 00:19:02,720
So we give it a target number of clusters.

432
00:19:02,720 --> 00:19:06,080
But how does it actually go about grouping the data?

433
00:19:06,080 --> 00:19:08,720
So imagine it's like a game of capture the flag.

434
00:19:08,720 --> 00:19:09,200
OK.

435
00:19:09,200 --> 00:19:09,840
I like that.

436
00:19:09,840 --> 00:19:12,320
You start by randomly placing K flags on the field.

437
00:19:12,320 --> 00:19:14,160
Those are our initial cluster centers.

438
00:19:14,160 --> 00:19:17,360
Then each data point runs to join the closest flag.

439
00:19:17,360 --> 00:19:17,920
OK.

440
00:19:17,920 --> 00:19:22,480
After everyone's picked a team, you move the flags to the center of their respective groups.

441
00:19:22,480 --> 00:19:26,320
OK. So the flags reposition based on where their team members are.

442
00:19:26,320 --> 00:19:27,040
Exactly.

443
00:19:27,040 --> 00:19:30,960
And then players can switch teams if a different flag is now closer.

444
00:19:30,960 --> 00:19:34,080
And this process continues until everyone settles down.

445
00:19:34,080 --> 00:19:39,520
So we end up with K clusters, where the data points within each cluster are more like each other

446
00:19:39,520 --> 00:19:42,400
than they are to the points in other clusters.

447
00:19:42,400 --> 00:19:42,880
Exactly.

448
00:19:42,880 --> 00:19:45,680
It's a pretty simple concept, but a very powerful technique.

449
00:19:45,680 --> 00:19:49,120
So are there other clustering algorithms besides K-means?

450
00:19:49,120 --> 00:19:50,960
There are quite a few.

451
00:19:50,960 --> 00:19:57,120
Some algorithms like hierarchical clustering don't require us to specify the number of clusters beforehand.

452
00:19:57,120 --> 00:19:57,760
Oh, interesting.

453
00:19:57,760 --> 00:20:03,520
They build a kind of family tree of clusters, allowing us to see different levels of relationships in the data.

454
00:20:03,520 --> 00:20:06,720
Oh, so we can choose how granular we want our clusters to be.

455
00:20:06,720 --> 00:20:07,680
Yeah, exactly.

456
00:20:07,680 --> 00:20:08,160
Cool.

457
00:20:08,160 --> 00:20:13,440
And then there are also algorithms like DBS-Scan that can handle clusters with irregular shapes

458
00:20:13,440 --> 00:20:15,600
and do a better job of dealing with outliers.

459
00:20:15,600 --> 00:20:16,240
Outliers.

460
00:20:16,240 --> 00:20:18,960
Those data points that don't seem to fit in anywhere.

461
00:20:18,960 --> 00:20:19,360
OK.

462
00:20:19,360 --> 00:20:22,400
It's especially useful when those clusters aren't neatly separated.

463
00:20:22,400 --> 00:20:24,960
Right. It's like finding constellations in the night sky.

464
00:20:24,960 --> 00:20:26,320
They're not always perfect shapes.

465
00:20:26,320 --> 00:20:26,560
Yeah.

466
00:20:26,560 --> 00:20:30,960
But we can still group those stars together based on, you know, proximity and stuff.

467
00:20:30,960 --> 00:20:31,760
Exactly.

468
00:20:31,760 --> 00:20:32,160
Yeah.

469
00:20:32,160 --> 00:20:36,160
So clustering is a really powerful tool for finding hidden groups in your data.

470
00:20:36,160 --> 00:20:36,720
This is great.

471
00:20:36,720 --> 00:20:37,520
Great.

472
00:20:37,520 --> 00:20:42,480
So I'm ready to explore another area of unsupervised learning what's next on the list.

473
00:20:42,480 --> 00:20:45,360
Let's shift gears and talk about dimensionality reduction.

474
00:20:45,360 --> 00:20:46,560
Dimensionality reduction.

475
00:20:46,560 --> 00:20:48,800
That sounds like something straight out of a sci-fi movie.

476
00:20:48,800 --> 00:20:51,840
It does, but it's more like decluttering a messy room.

477
00:20:51,840 --> 00:20:52,320
OK.

478
00:20:52,320 --> 00:20:53,040
I like that.

479
00:20:53,040 --> 00:20:59,200
So in dimensionality reduction, we try to reduce the number of features or dimensions in our data set

480
00:20:59,200 --> 00:21:03,040
while still preserving as much of the important information as possible.

481
00:21:03,040 --> 00:21:03,440
OK.

482
00:21:03,440 --> 00:21:06,640
I'm all for decluttering, but why would we want to get rid of features?

483
00:21:06,640 --> 00:21:08,480
Is it more information always better?

484
00:21:08,480 --> 00:21:09,920
In theory, yes.

485
00:21:09,920 --> 00:21:14,480
But in practice, too many features can make it harder to find meaningful patterns.

486
00:21:14,480 --> 00:21:15,040
Right.

487
00:21:15,040 --> 00:21:22,720
Imagine trying to find a specific book in a library with millions of books, but no organization system.

488
00:21:22,720 --> 00:21:23,040
Right.

489
00:21:23,040 --> 00:21:23,760
It'd be overwhelming.

490
00:21:23,760 --> 00:21:24,560
It'd be overwhelming.

491
00:21:24,560 --> 00:21:28,240
Dimensionality reduction helps us simplify our data and make it more manageable.

492
00:21:28,240 --> 00:21:32,720
So it's like creating that cataloging system for the library so we can actually find the books we need.

493
00:21:32,720 --> 00:21:33,520
Exactly.

494
00:21:33,520 --> 00:21:37,520
Too much clutter can just make it hard to focus on what's important.

495
00:21:37,520 --> 00:21:37,840
I see.

496
00:21:37,840 --> 00:21:41,840
So how do we actually go about reducing the dimensionality of our data?

497
00:21:41,840 --> 00:21:46,960
Well, one of the most popular and powerful techniques is called principal component analysis, or PCA.

498
00:21:46,960 --> 00:21:48,400
Principal component analysis.

499
00:21:48,400 --> 00:21:48,960
Yeah.

500
00:21:48,960 --> 00:21:49,520
OK.

501
00:21:49,520 --> 00:21:50,720
That sounds very official.

502
00:21:50,720 --> 00:21:52,240
What's the principle behind it?

503
00:21:52,240 --> 00:21:55,040
Imagine you're looking at a cloud of data points.

504
00:21:55,040 --> 00:21:55,440
OK.

505
00:21:55,440 --> 00:22:01,440
PCA tries to find the directions in which the data is most spread out or has the most variance.

506
00:22:01,440 --> 00:22:02,000
OK.

507
00:22:02,000 --> 00:22:04,640
These directions are called principal components.

508
00:22:04,640 --> 00:22:05,200
Let's get it.

509
00:22:05,200 --> 00:22:08,480
Think of it like finding the longest and widest dimensions of a box.

510
00:22:08,480 --> 00:22:12,560
So it's about finding the axes along which the data varies the most.

511
00:22:12,560 --> 00:22:13,280
Exactly.

512
00:22:13,280 --> 00:22:18,160
And the cool thing is that these principal components are often combinations of the original features.

513
00:22:18,160 --> 00:22:18,640
OK.

514
00:22:18,640 --> 00:22:22,800
And we can rank them by how much of the variance in the data they explain.

515
00:22:23,440 --> 00:22:29,120
Often just a few principal components can capture a surprisingly large percentage of the total variance.

516
00:22:29,120 --> 00:22:34,320
So we can condense the information from many features into a smaller set.

517
00:22:34,320 --> 00:22:34,720
Exactly.

518
00:22:34,720 --> 00:22:36,240
Without losing too much information.

519
00:22:36,240 --> 00:22:36,640
Exactly.

520
00:22:36,640 --> 00:22:39,280
And that can make our data much easier to work with.

521
00:22:39,280 --> 00:22:39,440
Right.

522
00:22:39,440 --> 00:22:42,640
And it can even improve the performance of machine learning models.

523
00:22:42,640 --> 00:22:42,960
Wow.

524
00:22:42,960 --> 00:22:45,200
So it's like distilling the essence of the data.

525
00:22:45,200 --> 00:22:45,680
Yes.

526
00:22:45,680 --> 00:22:47,520
Into a more concentrated form.

527
00:22:47,520 --> 00:22:48,240
Exactly.

528
00:22:48,240 --> 00:22:50,720
Can you give us an example of how PCA might be used?

529
00:22:50,720 --> 00:22:50,960
Yeah.

530
00:22:50,960 --> 00:22:52,880
Let's say you're working with image data.

531
00:22:52,880 --> 00:22:53,200
OK.

532
00:22:53,200 --> 00:22:56,160
And each image is represented by thousands of pixels.

533
00:22:56,160 --> 00:22:56,480
Right.

534
00:22:56,480 --> 00:22:57,360
That's a lot of features.

535
00:22:57,360 --> 00:22:58,400
That's a lot of features.

536
00:22:58,400 --> 00:23:02,880
PCA could help you find the most important patterns in those pixel values.

537
00:23:02,880 --> 00:23:03,520
OK.

538
00:23:03,520 --> 00:23:07,600
And represent each image with a smaller set of principal components.

539
00:23:07,600 --> 00:23:09,920
So instead of having thousands of pixel values.

540
00:23:09,920 --> 00:23:10,320
Right.

541
00:23:10,320 --> 00:23:14,480
We'd have a smaller set that capture the most important visual information.

542
00:23:14,480 --> 00:23:15,040
Exactly.

543
00:23:15,040 --> 00:23:18,480
It's like compressing a large image file without losing too much quality.

544
00:23:18,480 --> 00:23:19,200
Right.

545
00:23:19,200 --> 00:23:19,760
Very cool.

546
00:23:20,560 --> 00:23:21,840
So we've talked about clustering.

547
00:23:21,840 --> 00:23:23,920
We've talked about dimensionality reduction.

548
00:23:23,920 --> 00:23:24,400
Mm-hmm.

549
00:23:24,400 --> 00:23:28,240
Are there any other key concepts in unsupervised learning?

550
00:23:28,240 --> 00:23:29,680
Those are the main highlights.

551
00:23:29,680 --> 00:23:33,920
You know, unsupervised learning is really all about letting the algorithm take the lead.

552
00:23:33,920 --> 00:23:34,240
Right.

553
00:23:34,240 --> 00:23:37,120
And discover the hidden structures in the data.

554
00:23:37,120 --> 00:23:38,560
It's about exploration.

555
00:23:38,560 --> 00:23:38,880
It is.

556
00:23:38,880 --> 00:23:40,880
It's a world of exploration and discovery.

557
00:23:40,880 --> 00:23:43,360
This has been a really eye-opening journey.

558
00:23:43,360 --> 00:23:44,160
It has.

559
00:23:44,160 --> 00:23:47,360
We've gone from supervised learning to unsupervised learning.

560
00:23:47,360 --> 00:23:47,840
But.

561
00:23:47,840 --> 00:23:51,120
Uncovered a whole treasure trove of techniques.

562
00:23:51,120 --> 00:23:51,600
Yeah.

563
00:23:51,600 --> 00:23:55,200
And what's even more exciting is that machine learning is constantly evolving.

564
00:23:55,200 --> 00:23:55,680
It is.

565
00:23:55,680 --> 00:23:58,080
There's always something new to learn and explore.

566
00:23:58,080 --> 00:24:02,560
Speaking of new discoveries, what are some of the things on the horizon of machine learning

567
00:24:02,560 --> 00:24:05,440
that have you particularly excited?

568
00:24:05,440 --> 00:24:05,680
Yeah.

569
00:24:06,320 --> 00:24:06,560
I know.

570
00:24:06,560 --> 00:24:08,080
We've only scratched the surface here.

571
00:24:08,720 --> 00:24:12,240
What would you say is a good next step for someone who wants to keep learning?

572
00:24:12,240 --> 00:24:16,880
Well, the source actually mentions a really great resource, the Psychitlearn Cheat Sheet.

573
00:24:16,880 --> 00:24:17,680
Oh yeah, I remember that.

574
00:24:17,680 --> 00:24:18,160
Yeah.

575
00:24:18,160 --> 00:24:21,680
And they talked about how it can help you figure out which algorithm might be the best

576
00:24:21,680 --> 00:24:23,360
for your specific problem.

577
00:24:23,360 --> 00:24:24,000
Exactly.

578
00:24:24,000 --> 00:24:28,880
It's a really helpful visual guide that can help you navigate the landscape of algorithms

579
00:24:28,880 --> 00:24:30,160
and make more informed choices.

580
00:24:30,880 --> 00:24:31,200
Right.

581
00:24:31,200 --> 00:24:35,360
And plus Psychitlearn is a powerful Python library for machine learning.

582
00:24:35,360 --> 00:24:37,840
So it's a great tool to add to your skill set.

583
00:24:37,840 --> 00:24:42,560
So armed with what we've learned in this deep dive and that cheat sheet,

584
00:24:43,280 --> 00:24:46,480
I feel like anyone could start experimenting with different algorithms.

585
00:24:46,480 --> 00:24:46,960
Absolutely.

586
00:24:46,960 --> 00:24:48,720
And that's the beauty of machine learning.

587
00:24:48,720 --> 00:24:49,920
It's a hands-on field.

588
00:24:49,920 --> 00:24:53,600
The more you experiment and play with different algorithms and different data sets,

589
00:24:53,600 --> 00:24:54,880
the more you'll learn.

590
00:24:54,880 --> 00:24:57,600
It's all about diving in and getting your hands dirty with the data.

591
00:24:57,600 --> 00:24:58,160
Exactly.

592
00:24:58,160 --> 00:24:59,840
Seeing what you can create.

593
00:24:59,840 --> 00:25:02,160
It reminds me of when I first started learning to code.

594
00:25:02,160 --> 00:25:02,720
Yeah.

595
00:25:02,720 --> 00:25:06,960
It can feel intimidating, but once you start building things, it's so much fun.

596
00:25:06,960 --> 00:25:07,840
I totally agree.

597
00:25:07,840 --> 00:25:12,960
And that sense of playfulness and curiosity is so important in machine learning because

598
00:25:12,960 --> 00:25:14,960
it's a field that's always evolving.

599
00:25:15,520 --> 00:25:18,800
You need to be comfortable with experimenting and trying new things.

600
00:25:18,800 --> 00:25:23,440
Speaking of new things, what are some of the areas of machine learning that you're

601
00:25:23,440 --> 00:25:24,960
most excited about?

602
00:25:24,960 --> 00:25:26,000
What's on the horizon?

603
00:25:26,000 --> 00:25:30,320
Well, one area that I think is really fascinating is the development of algorithms

604
00:25:30,320 --> 00:25:33,040
that can explain their decisions more transparently.

605
00:25:33,040 --> 00:25:33,440
Okay.

606
00:25:33,440 --> 00:25:37,440
You know, as we rely more and more on machine learning for things like medical diagnoses

607
00:25:37,440 --> 00:25:43,760
or loan approvals, it's becoming really important to understand how these models are arriving at

608
00:25:43,760 --> 00:25:44,960
their conclusions.

609
00:25:44,960 --> 00:25:45,200
Yeah.

610
00:25:45,200 --> 00:25:49,760
Explainable AI is becoming crucial, especially in those areas where the stakes are high.

611
00:25:49,760 --> 00:25:50,800
Exactly.

612
00:25:50,800 --> 00:25:52,240
What else has caught your eye?

613
00:25:52,240 --> 00:25:56,000
Another trend I'm excited about is the rise of federated learning.

614
00:25:56,000 --> 00:25:56,400
Okay.

615
00:25:56,400 --> 00:26:01,920
So it's a way to train models on decentralized data without compromising privacy.

616
00:26:01,920 --> 00:26:02,400
Interesting.

617
00:26:02,400 --> 00:26:09,520
Imagine being able to train a really powerful medical diagnosis model using data from hospitals

618
00:26:09,520 --> 00:26:14,640
all over the world without ever having to actually share that sensitive patient data.

619
00:26:14,640 --> 00:26:14,880
Yeah.

620
00:26:14,880 --> 00:26:17,680
That sounds like a game changer, especially in healthcare or finance.

621
00:26:17,680 --> 00:26:18,640
It really does.

622
00:26:18,640 --> 00:26:23,360
It's amazing to see how machine learning is not only advancing in terms of its capabilities,

623
00:26:23,360 --> 00:26:26,960
right, but also addressing ethical and societal concerns.

624
00:26:26,960 --> 00:26:30,160
It's a really exciting time to be involved in this field.

625
00:26:30,160 --> 00:26:34,560
And I think as machine learning continues to evolve, it's important to remember that it's

626
00:26:34,560 --> 00:26:36,480
not just about the algorithms themselves.

627
00:26:36,480 --> 00:26:36,960
Yeah.

628
00:26:36,960 --> 00:26:41,280
It's about how we use them to solve real world problems and make a positive impact.

629
00:26:41,920 --> 00:26:42,400
Well said.

630
00:26:42,400 --> 00:26:46,960
This has been such a rewarding deep dive into the world of machine learning algorithms.

631
00:26:46,960 --> 00:26:47,280
Yeah.

632
00:26:47,280 --> 00:26:51,440
I feel like I've gained a solid foundation and I'm excited to explore more.

633
00:26:51,440 --> 00:26:52,160
I know me too.

634
00:26:52,720 --> 00:26:54,640
It's been a pleasure exploring this world with you.

635
00:26:55,200 --> 00:26:56,080
Likewise.

636
00:26:56,080 --> 00:26:59,680
And to all of our listeners, thank you for joining us on this journey.

637
00:27:00,320 --> 00:27:02,880
Keep learning, keep experimenting.

638
00:27:02,880 --> 00:27:05,680
And who knows, maybe you'll be the one to create the next

639
00:27:05,680 --> 00:27:08,800
groundbreaking machine learning algorithm until next time.

640
00:27:08,800 --> 00:27:18,800
Happy exploring.