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What is machine learning? At its core, machine learning is about teaching computers to learn from experience.
A clear 12-minute explanation of machine learning, its core components, and how it powers AI using data, algorithms, models, and training.
Key Takeaways
- Machine learning enables computers to learn and improve from data without explicit instructions.
- High-quality data is more important than sheer quantity for effective machine learning.
- Algorithms act as the learning mechanism, tuning parameters to reduce prediction errors.
- Models represent learned knowledge and vary in complexity depending on the task.
- Training and evaluation are essential to develop and validate a reliable machine learning model.
Summary
- Machine learning teaches computers to learn from data and experience without explicit programming.
- It is a subset of artificial intelligence (AI), which broadly covers systems performing human-like tasks.
- Deep learning is a further subset of machine learning using neural networks to learn complex patterns.
- Four key components of machine learning systems are data, algorithms, models, and training/evaluation.
- Quality data is crucial and must be accurate, relevant, and clean to ensure effective learning.
- Algorithms process data to extract patterns and iteratively adjust parameters to improve predictions.
- The model is the output of the learning process, representing a mathematical function for predictions or classifications.
- Training involves exposing the model to data to learn and reduce errors, while evaluation tests its performance.
- Data is divided into training, validation, and test sets to optimize and assess the model.
- Loss functions and optimization methods like gradient descent guide the model to improve accuracy.
Full Transcript — Download SRT & Markdown
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But instead of giving a computer exact step-by-step instructions, [music] we let it figure things out by showing it examples and letting math do the rest.
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So basically, machine learning is when computers learn patterns from data and use them to make predictions or decisions without being explicitly programmed for every single step. It's kind of like how you learn not to touch a hot stove as a kid once you got
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burned. But before I really get into what machine learning is, [music] let me give you more of a contextual understanding. You might have heard of this little thing called AI over the past few years. I mean, it's not like
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it's ever mentioned. Okay? Yeah, it's everywhere. If buzzwords had a leaderboard, AI would be number one. But the thing about AI is that it's a very broad field. At its core, AI or artificial intelligence is about building systems that can perform
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tasks that normally require human intelligence. Things like recognizing [music] speech, making decisions, answering questions, or suggesting what you should watch next when you really should be going to bed.
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Okay, cool. So, [music] what does this have to do with machine learning? Well, it turns out that machine learning is a subset of AI. It's basically the engine that powers AI's digital brain to actually learn and improve.
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Basically, if AI is about simulating human intelligence, then machine learning is the way to actually make that happen.
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Instead of explicit programming, machine learning uses algorithms to analyze large amounts of data in order to learn and improve from experience. Yep.
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Along with AI and machine learning, there is an even further subset of machine learning called deep learning that uses layered neural networks inspired by the human brain, [music] which learns even more complex patterns from data. Feel free to comment below if
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you want a separate video for deep learning, as it's a beast of a topic on its own.
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Okay, now that we've established what machine learning is, let's talk about the core components that make a machine learning system actually work. To keep it simple, we will focus on four key areas: data, algorithms, models, and training and evaluation.
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First up, data. Data is like the ingredients that the machine learning feeds on. Without data, there's literally nothing for the machines to learn from. No [music] data would be like having a car with no gasoline. But here's the thing. While you need a lot
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of data to train your machine, the quality of your data can be more important than the quantity. You put garbage data in, you often get garbage data out. For example, if you have a data set that's full of errors, biases,
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or irrelevant features, well, no algorithm can fix that. The importance of having quality data is largely the reason why data analysts and data scientists exist.
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So, what does quality data look like? Quality data has three things: accuracy, [music] which means the data should represent reality as closely as possible; relevance, which means the [music] features should directly relate to the prediction task; and cleanliness,
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which removes duplicates, fixes typos, and handles missing [music] values. Also, more data generally equals better performance because it covers more scenarios for edge cases and lets the model learn subtle relationships it couldn't spot with a limited sample size.
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But more data only helps if it's good data. Millions of noisy or irrelevant data features will actually work against your goals.
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Next up, we have [music] algorithms. Algorithms are the learning process where we actually extract meaningful insights from the data. It's a set of rules and calculations we use to extract patterns, structures, or decision strategies from the data set. If data is
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like the ingredients that machine learning needs, [music] then algorithms are kind of like the chef that turns the raw data into something useful.
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There are tons of different algorithms in machine learning such as linear regression, logistic regression, principal component analysis, clustering, anomaly detection.
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Basically, there's way too many [music] algorithms to cover in the scope of this one video. But the point is that the algorithms vary by task. Some algorithms are good for prediction, others for pattern discovery, and others for trial
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and error learning. These algorithms iteratively adjust their internal parameters called weights and biases to improve their accuracy.
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It's kind of like tuning an old AM/FM radio. At first, you hear a bunch of static, but you adjust the dials with tiny [music] nudges until the station comes through. That's basically what a learning algorithm does. It keeps
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tweaking its internal parameters step by step to reduce the noise or the errors until the predictions come through clearly.
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The next component is the model. If data is the ingredients and [music] the algorithm is the chef, then the model is like the dish. It's what you get after the algorithm is done learning.
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Basically, the model is a mathematical function that takes input and produces an output. Whether that's a prediction, classification, [music] recommendation, or action, all based on what it learned from the training data.
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[music] For example, your model can predict whether your emails are spam or not spam, which is called classification.
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Or you can train your model to estimate the price of a house based on the [music] details of a home, which is something called regression. The complexity of the model can vary. It really depends on your end goal. So, for
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example, these models can range from a simple straight line regression equation to complex deep networks with billions of parameters. That complexity depends on your problem, data scale, and depth of your pockets.
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In short, the model is basically the brain that is the result of your training data and your algorithm.
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Lastly, [music] we have training and evaluation. Training is the model's practice phase. This is where the algorithm has access to data and uses it to learn patterns, adjust parameters, and of course, [music] get better at making predictions or decisions. Machine
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learning models don't start knowing the rules. They discover them from exposure to more quality data. Just like how a pro boxer needs to train to improve and sharpen his skills, the model needs training as well. Otherwise, it will end
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up just making random guesses. Wrong. [music] Each cycle of training reduces error and improves the ability of the model.
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Evaluation is exactly how it sounds, which [music] is to assess how our model is doing. If our model is like the dish, then the evaluation is like the taste test. It's where we find out if all that training actually resulted in a capable
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model. In practice, you will typically see training and evaluation performed by dividing up the data into three categories: the training set, which is used to initially teach the model using the data; the validation set, which is used to tune hyperparameters like the
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learning rate; and a test [music] set, which is used to measure the performance of your model.
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The model starts with random or default parameters. As data flows through the algorithm, predictions are made. A math formula called a loss function, which might look something like this, is used to measure how wrong the predictions are. In case this equation you see on
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screen makes you want to go, "Oh, hell no." Don't worry, I'll make it sound super simple. This equation is a way of telling the [music] model just how badly it messed up. It's kind of like a parent telling their kid, "You were this close.
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Try again." Basically, a loss function tells the model what it needs to fix. After that, an optimization method such as [music] gradient descent is used to adjust parameters to reduce that loss.
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This process repeats itself a bunch of times across the training set until performance reaches an acceptable level.
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So to sum it all up,
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The types of machine learning you'll hear most often about are supervised, unsupervised reinforcement and semi-supervised learning.
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Supervised learning is when the machine learning model learns from labeled examples. Unsupervised learning is when the model finds patterns [music] or structure in data without any labels.
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Reinforcement learning is when the model learns by taking actions and receiving rewards or penalties from the environment. And in semi-supervised learning, the model learns from a combination of labeled and unlabeled data. In supervised learning, for example, if you have photos of apples
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and bananas with the correct labels, eventually the model will be able to look at a brand new photo and determine whether it's an apple or a banana. This is what's known as classification. When you predict a discrete output, if you
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feed the model thousands of housing [music] details and prices, it learns to estimate a price for a new house. This is known as regression, where you predict a continuous output. It's kind of like studying for a test with all the
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answers. Unsupervised learning is where there are no correct answers provided, [music] meaning it's just raw data with no labels. So, what does the model do?
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Well, the model's job here is to spot structure. Here, the focus is to group things that are similar, [music] find patterns, and detect anomalies. It initially has no idea that these are circles [music] squares, and triangles. But it learns to
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group them together based on similar patterns, [music] such as the color of the circles being blue or the squares having four sides. Unsupervised learning is kind of like walking into a room full of people and figuring out which ones
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are introverts versus extroverts based on their behavior patterns. Reinforcement learning is a bit different. It's all about trial and error. Here, there's something called an agent that interacts with an environment, takes actions, gets rewards or penalties, and then tunes its
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strategy, which is something called a policy, in order to maximize long-term reward. It's a lot like learning how to play a difficult video game. You die repeatedly, but you get slightly further each time until you master the level.
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Here's a real world example of reinforcement learning in action. As you can see, the robot failed at first, [music] but eventually was successful at walking over the obstacle, much like humans attempting things for the first time before getting good at it. [music]
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And lastly, we have semi-supervised learning. Semi-supervised learning is like the fusion between supervised and unsupervised [music] learning. It's exactly what it sounds like. You have some labeled data, but not enough to train a great supervised model. So you
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let the algorithm use the unlabeled data to learn additional structure and improve [music] performance. For instance, if you have a few photos of dogs and cats, each [music] with labels, the model will look at these labels and features and use what it learned to try
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and identify the unlabeled ones. Think of these as different ways to get good at something. Kind of like the way we humans learn, either by having a teacher, figuring stuff on our own, or just messing around until we finally get
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it right. And now you're smarter in machine learning than you were 10 [music] minutes ago. Congratulations.
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Machine learning is a fascinating and constantly evolving world. But at the core of it, it isn't some mysterious black box or magic. It's really just data, algorithms, and some trial and error doing their thing.
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If you want to learn more about machine learning, I've got a free ebook that breaks machine learning down in a super simple and easy way. It goes over all the concepts that I've covered here, but it also does a deeper dive into the
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different [music] algorithms used as well as cover some more of the fundamentals of machine learning. It's called machine learning simplified. No scary math or complex jargon. You can click the link in the description or scan the QR code on screen to get your
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copy. Go ahead and read it at your own pace. And again, it's totally [music] free. If you like this video, please like and subscribe and leave a comment down below of what topic you'd like to see covered next. I'll see you guys in the next
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video. [music]
Topics:machine learningartificial intelligencedeep learningdata qualityalgorithmsmodelstrainingevaluationgradient descentloss function
