Machine Learning Basics: Supervised Learning
Key Points
- AI is the broad concept of machines mimicking human problem‑solving, with machine learning (ML) as a data‑driven subset that learns from examples, and deep learning as a further subset that automates feature extraction for massive datasets.
- The talk focuses on ML, specifically its two main supervised learning approaches: classification (grouping data into predefined categories) and regression (modeling relationships with weighted input variables).
- A real‑world classification example is customer churn prediction, where labeled historical customer behavior trains a model to flag likely churners so businesses can intervene and retain them.
- Regression techniques build mathematical equations that assign weights to input features to predict continuous outcomes, enabling more nuanced forecasting beyond simple category assignment.
Sections
- AI vs. ML vs. Deep Learning - An IBM Data Platform Solution Engineer clarifies the differences among artificial intelligence, machine learning, and deep learning before introducing supervised learning as a primary machine‑learning approach.
- Regression and Clustering in ML - The passage explains regression for predicting airline ticket prices and then introduces unsupervised clustering for customer segmentation.
- Reinforcement Learning for Autonomous Driving - The speaker explains reinforcement learning as a semi‑supervised method where an agent learns via rewards and penalties in an environment, illustrating it with self‑driving cars and urging viewers to explore machine‑learning concepts further.
Full Transcript
# Machine Learning Basics: Supervised Learning **Source:** [https://www.youtube.com/watch?v=9gGnTQTYNaE](https://www.youtube.com/watch?v=9gGnTQTYNaE) **Duration:** 00:08:22 ## Summary - AI is the broad concept of machines mimicking human problem‑solving, with machine learning (ML) as a data‑driven subset that learns from examples, and deep learning as a further subset that automates feature extraction for massive datasets. - The talk focuses on ML, specifically its two main supervised learning approaches: classification (grouping data into predefined categories) and regression (modeling relationships with weighted input variables). - A real‑world classification example is customer churn prediction, where labeled historical customer behavior trains a model to flag likely churners so businesses can intervene and retain them. - Regression techniques build mathematical equations that assign weights to input features to predict continuous outcomes, enabling more nuanced forecasting beyond simple category assignment. ## Sections - [00:00:00](https://www.youtube.com/watch?v=9gGnTQTYNaE&t=0s) **AI vs. ML vs. Deep Learning** - An IBM Data Platform Solution Engineer clarifies the differences among artificial intelligence, machine learning, and deep learning before introducing supervised learning as a primary machine‑learning approach. - [00:03:20](https://www.youtube.com/watch?v=9gGnTQTYNaE&t=200s) **Regression and Clustering in ML** - The passage explains regression for predicting airline ticket prices and then introduces unsupervised clustering for customer segmentation. - [00:06:35](https://www.youtube.com/watch?v=9gGnTQTYNaE&t=395s) **Reinforcement Learning for Autonomous Driving** - The speaker explains reinforcement learning as a semi‑supervised method where an agent learns via rewards and penalties in an environment, illustrating it with self‑driving cars and urging viewers to explore machine‑learning concepts further. ## Full Transcript
Hey, what's up everyone?
My name is Luv Aggarwal, and I’m a Data Platform Solution engineer for IBM.
Machine Learning.
There's no doubt that this is an incredibly hot topic with significant interest from both
business professionals as well as technologists. So let's talk about what machine learning,
or ML, is.
So, before we get too far into the details, I want to take a minute to talk about some
terms that are often used interchangeably but have certain differences.
Terms like “artificial intelligence”, “machine learning”, and even “deep learning”.
So, at the highest level, AI is defined as leveraging computers or machines to mimic
the problem-solving and the decision-making capabilities of the human mind.
And machine learning is a subset within AI that's more focused on the use of various self-learning
algorithms that derive knowledge from data in order to predict outcomes.
And then, finally, deep learning is a further subset within even machine learning, and deep
learning is often thought of as scalable machine learning because it automates a lot of the
feature extraction process away and eliminates the some of the human intervention involved
to enable the use of some really, really big data sets.
But for today we'll focus just on machine learning, so we'll get rid of the other two
and dive one level deeper and talk about the different types of machine learning.
Ok. So, the first type that we have is called “supervised learning”.
And this is when we use labeled data sets to train algorithms to classify data or predict outcomes.
And when I say labeled, I mean that the rows in the data set are labeled, tagged, or classified
in some interesting way that tells us something about that data.
So, it could be a yes or a no, or it could be a particular category of some, you know,
different attribute.
OK, so how do we apply supervised machine learning techniques?
Well, this really depends on your particular use-case.
We could be using a classification model
which recognizes and groups ideas or objects into predefined categories.
An example of this in the real world is with customer retention.
So, if you're in the business of managing customers, one of your goals is typically
minimizing and identifying customer churn, right, which are customers that no longer
buy a particular product or service, and we want to avoid churn because it's almost always
more costly to acquire a new customer than it is to retain an existing one, right?
So, if we have historical data for the customer, like their activity - whether they churned
or not, right - we can build a classification model using supervised machine learning, and
our labeled data set that will help us identify customers that are about to churn, and then
allow us to take action to retain them.
OK, so the other type of supervised learning is regression.
Now, this is when we build an equation using various input values with their specific weights
determined by the overall value of their impact on the outcome.
And we use these to generate an estimate for an output value.
So, let me give you another example here.
So, airlines rely heavily on machine learning, and they use regression techniques to accurately
predict how much they should be charging for a particular flight, right?
So, they use various input factors like, you know, days before departure, the day of the week,
the departure, the destination to use these to predict an accurate dollar value
for how much they should be charging for a specific flight that will maximize their revenue.
OK, so now let's move on to the second type of machine learning which is
“unsupervised learning”.
OK, so this is when we use machine learning algorithms to analyze and cluster unlabeled
data sets, and this method helps us discover hidden patterns or groupings without the need
for human intervention, right?
So, we're using unlabeled data here.
So, again, let's talk about the different techniques for unsupervised learning.
One method is “clustering”.
And a real-world example of this is when organizations try to do
customer segmentation.
Right.
So, when businesses try to do effective marketing it's really critical that they really understand
who their customers are, right, so that they can connect with them in the most relevant way.
And, oftentimes, it's not obvious or clear how certain customers are similar to or different
from one another, right, and clustering algorithms can help take into account a variety of information
on the customer like their purchase history,
you know, their social media activity, or website activity,
could be their geography, and much more, to group similar customers
into buckets so that we can send them more relevant offers, provide them better customer
service, and be more targeted with our marketing efforts.
Ok.
And the last point I want to touch on for unsupervised learning is
called “dimensionality reduction”.
So, we won't discuss this in detail in this video, but this refers to techniques that
reduce the number of input variables in a data set so we don't let some redundant parameters
over represent the impact on the on the outcome.
Ok.
So the last type of machine learning I want to talk about today is called
“reinforcement learning”.
Now, this is a form of semi-supervised learning where we typically have an agent or system
take actions
in an environment.
Now the environment will then either reward the agent for correct moves,
or punish it for incorrect moves. Right.
And, through many iterations of this, we can teach a system a particular task.
Now a great example of this method in the real world is with self-driving cars.
So, autonomous driving has several factors, right?
There's the speed limit, there are drivable zones, there are collisions, and so on.
So, we can use forms of reinforcement learning to teach a system how to drive by avoiding
collisions, following the speed limit, and so on.
OK, so we covered many topics today, but you know,
we've barely scratched the surface of each one.
If you found any one particular aspect of machine learning interesting, I encourage
you to dive deeper and learn more about it. And if you want to know what are some of the
common machine learning algorithms and how to leverage them in data science, please check
out some of the links in the description.
Thank you.
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