What is Machine Learning?

Summary

In this informative video, Luv Aggarwal from IBM discusses the basics of machine learning, distinguishing it from AI and deep learning. He explores supervised learning, including classification and regression, unsupervised learning with clustering, and dimensionality reduction, and concludes with reinforcement learning. The video encourages deeper exploration into machine learning and its applications.

Takeaways

• 👋 Introduction: Luv Aggarwal, Data Platform Solution Engineer at IBM, discusses machine learning (ML).
• 💡 AI vs. ML vs. Deep Learning: AI mimics human problem-solving; ML, a subset of AI, uses algorithms to predict outcomes; Deep Learning, a subset of ML, automates feature extraction for big data.
• 📊 Supervised Learning: Uses labeled data to train algorithms for classification and regression tasks.
• 🔍 Classification Example: Identifying customer churn using historical data to retain customers.
• 📈 Regression Example: Airlines predicting flight prices using input factors to maximize revenue.
• 🧩 Unsupervised Learning: Analyzes and clusters unlabeled data to discover hidden patterns.
• 👥 Clustering Example: Customer segmentation for targeted marketing using purchase history and other data.
• 🔻 Dimensionality Reduction: Techniques that reduce input variables in a data set to avoid redundancy.
• 🤖 Reinforcement Learning: Semi-supervised learning where an agent learns tasks through rewards and punishments.
• 🚗 Real-World Example: Self-driving cars using reinforcement learning to avoid collisions and follow traffic rules.
• 📚 Further Learning: Encourages viewers to explore specific aspects of ML further and provides resources in the video description.
• 👍 Call to Action: Invites viewers to like, subscribe, and check out IBM Cloud Labs for interactive learning.

Q & A

What is the primary focus of the speaker in this video script?

The speaker, Luv Aggarwal, focuses on explaining the concepts of artificial intelligence, machine learning, and deep learning, with a particular emphasis on machine learning and its different types.

How does the speaker define artificial intelligence (AI)?

AI is defined as leveraging computers or machines to mimic the problem-solving and decision-making capabilities of the human mind.

What is the relationship between AI, machine learning, and deep learning?

AI is a broad concept that includes machine learning, which is a subset focused on self-learning algorithms that derive knowledge from data. Deep learning is a further subset within machine learning, often considered scalable machine learning due to its automation of feature extraction.

What is supervised learning in the context of machine learning?

Supervised learning is a type of machine learning where labeled data sets are used to train algorithms to classify data or predict outcomes.

Can you provide an example of how supervised learning is applied in a real-world scenario?

An example is customer retention in businesses, where historical data of customers is used to build a classification model that identifies customers likely to churn, allowing businesses to take action to retain them.

What is the difference between classification and regression in supervised learning?

Classification involves recognizing and grouping ideas or objects into predefined categories, while regression involves building an equation using input values and their weights to estimate an output value.

How do airlines use regression techniques in machine learning?

Airlines use regression techniques to predict the optimal price for a flight by considering various input factors such as days before departure, day of the week, and destination.

What is unsupervised learning and how does it differ from supervised learning?

Unsupervised learning involves using machine learning algorithms to analyze and cluster unlabeled data sets, discovering hidden patterns or groupings without human intervention, unlike supervised learning which uses labeled data.

What is clustering in unsupervised learning and how is it used in real-world scenarios?

Clustering is a method in unsupervised learning that groups similar data points together. It is used in customer segmentation to understand customer behavior and preferences, allowing businesses to tailor marketing efforts more effectively.

What is dimensionality reduction and how does it relate to unsupervised learning?

Dimensionality reduction is a technique in unsupervised learning that reduces the number of input variables in a data set, eliminating redundant parameters and focusing on the most impactful variables.

What is reinforcement learning and how does it differ from other types of machine learning?

Reinforcement learning is a form of semi-supervised learning where an agent or system takes actions in an environment and learns through rewards or punishments. It differs from other types of machine learning as it involves learning through trial and error in a dynamic environment.

How is reinforcement learning applied in the context of self-driving cars?

Reinforcement learning is used in self-driving cars to teach the system how to drive by avoiding collisions, following speed limits, and adhering to drivable zones through iterative learning and feedback.

Outlines

00:00

🤖 Introduction to Machine Learning

Luv Aggarwal, a Data Platform Solution engineer at IBM, introduces the topic of machine learning, noting its growing interest among business professionals and technologists. He explains the distinctions between artificial intelligence, machine learning, and deep learning, emphasizing that AI mimics human problem-solving, ML uses self-learning algorithms to predict outcomes, and deep learning automates feature extraction. The focus of the video will be on machine learning.

05:05

📊 Supervised Learning Explained

Aggarwal describes supervised learning, where labeled datasets train algorithms to classify data or predict outcomes. He explains that labeled data is categorized to reveal specific information. He provides examples like customer retention, where classification models predict customer churn, and airlines using regression techniques to set flight prices based on various factors.

🔍 Unsupervised Learning Insights

Unsupervised learning involves analyzing and clustering unlabeled datasets to uncover hidden patterns without human intervention. Aggarwal highlights techniques like clustering, used for customer segmentation to tailor marketing efforts. He briefly mentions dimensionality reduction, which simplifies datasets by reducing input variables, preventing redundant parameters from skewing results.

🚗 Reinforcement Learning and Real-World Applications

Aggarwal discusses reinforcement learning, a semi-supervised method where an agent learns through rewards and punishments in an environment. He uses self-driving cars as an example, where the system learns to navigate by avoiding collisions and adhering to speed limits. This method iterates to teach specific tasks effectively.

📚 Conclusion and Further Learning Resources

Aggarwal concludes by encouraging viewers to explore specific aspects of machine learning that interest them. He mentions additional resources and links for learning more about common machine learning algorithms and their applications in data science. He invites viewers to ask questions, like, and subscribe for more videos, and promotes IBM Cloud Labs for interactive learning.

Keywords

💡Machine Learning

Machine Learning (ML) is a subset of Artificial Intelligence (AI) that focuses on the development of computer programs that can access data and learn from it. In the video, ML is defined as using self-learning algorithms to derive knowledge from data to predict outcomes. It is central to the video's theme, as it is the main topic being discussed and explored.

💡Artificial Intelligence

Artificial Intelligence (AI) is the broader field of computer science that aims to create machines capable of performing tasks that would typically require human intelligence. In the script, AI is described as leveraging computers or machines to mimic human problem-solving and decision-making capabilities. It sets the stage for the discussion on ML and deep learning.

💡Deep Learning

Deep Learning is a subset of Machine Learning that involves the use of neural networks with many layers (deep neural networks) to model and understand complex patterns in data. The script mentions deep learning as a further subset within ML, emphasizing its role in automating feature extraction and handling large datasets.

💡Supervised Learning

Supervised Learning is a type of ML where an algorithm is trained using labeled data. The script explains that in supervised learning, data sets are labeled, and algorithms are trained to classify data or predict outcomes. It is exemplified by customer retention and flight pricing scenarios.

💡Labeled Data

Labeled Data refers to data that has been tagged or classified in a way that provides information about the data. In the context of the video, labeled data is crucial for training supervised learning models, as it helps the algorithm understand what each piece of data represents.

💡Classification Model

A Classification Model is a type of supervised learning algorithm used to predict categories or classes for data points. The script uses the example of customer churn to illustrate how a classification model can identify customers likely to leave, aiding in retention strategies.

💡Regression

Regression is another type of supervised learning where the goal is to predict a continuous outcome. The script mentions airlines using regression techniques to predict flight prices, demonstrating how regression can be used to estimate output values based on input factors.

💡Unsupervised Learning

Unsupervised Learning involves analyzing and clustering unlabeled data sets to discover hidden patterns or groupings. The script explains that unsupervised learning does not require human intervention to identify patterns, as seen in customer segmentation examples.

💡Clustering

Clustering is a technique in unsupervised learning that groups data points with similar characteristics together. The video script uses customer segmentation as an example of clustering, where businesses can better understand and target their customers by grouping them based on similar behaviors or characteristics.

💡Dimensionality Reduction

Dimensionality Reduction is a technique used to reduce the number of random variables under consideration and can be part of unsupervised learning. Although not discussed in detail in the script, it is mentioned as a method to simplify data sets by eliminating redundant parameters.

💡Reinforcement Learning

Reinforcement Learning is a type of semi-supervised learning where an agent learns to make decisions by taking actions in an environment and receiving rewards or penalties. The script uses the example of self-driving cars, where reinforcement learning can teach a system to drive by avoiding collisions and following rules.

Highlights

Introduction to Machine Learning by Luv Aggarwal, a Data Platform Solution engineer for IBM.

Machine Learning is a hot topic with significant interest from both business professionals and technologists.

Definition of Artificial Intelligence (AI) as leveraging computers to mimic human problem-solving and decision-making.

Machine Learning (ML) is a subset of AI focused on self-learning algorithms that derive knowledge from data to predict outcomes.

Deep Learning is a subset of ML, often considered scalable ML due to its automation of feature extraction.

Focus on Machine Learning, excluding AI and Deep Learning for the discussion.

Introduction to Supervised Learning using labeled datasets to train algorithms for classification or prediction.

Labeled data sets in supervised learning provide information about the data through tags or classifications.

Application of supervised learning in customer retention by building classification models to identify potential churn.

Regression in supervised learning involves building equations to estimate output values based on input factors.

Example of regression in airlines predicting flight prices using various input factors.

Introduction to Unsupervised Learning using algorithms to analyze and cluster unlabeled data sets.

Unsupervised Learning helps discover hidden patterns without human intervention through clustering.

Customer segmentation as an example of clustering in unsupervised learning for effective marketing.

Dimensionality Reduction in unsupervised learning reduces input variables to avoid redundant parameters.

Introduction to Reinforcement Learning as a form of semi-supervised learning involving an agent taking actions in an environment.

Self-driving cars as an example of reinforcement learning teaching systems to drive through avoiding collisions and following speed limits.

Encouragement to dive deeper into machine learning and explore common algorithms for data science.

Invitation to engage with IBM Cloud Labs for skill growth and badge earning.

Transcripts

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.

If you have questions please drop us a line below, and if you want to see more

videos like this in the future, please like and subscribe.

And don't forget, you can grow your skills and earn a badge with IBM Cloud Labs,

which are free browser-based interactive Kubernetes labs.