Machine Learning

Oracle University Podcast

Does machine learning feel like too convoluted a topic? Not anymore!

Listen to hosts Lois Houston and Nikita Abraham, along with Senior Principal OCI Instructor Hemant Gahankari, talk about foundational machine learning concepts and dive into how supervised learning, unsupervised learning, and reinforcement learning work.

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Special thanks to Arijit Ghosh, David Wright, Himanshu Raj, and the OU Studio Team for helping us create this episode.

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Episode Transcript:

00:00

Welcome to the Oracle University Podcast, the first stop on your cloud journey. During this

series of informative podcasts, we’ll bring you foundational training on the most popular

Oracle technologies. Let’s get started!

00:26

Lois: Hello and welcome to the Oracle University Podcast. I’m Lois Houston, Director of Innovation Programs with Oracle University, and with me is Nikita Abraham, Principal

Technical Editor.

Nikita: Hi everyone! Last week, we went through the basics of artificial intelligence and we’re going to take it a step further today by talking about some foundational machine learning concepts. After that, we’ll discuss the three main types of machine learning models: supervised learning, unsupervised learning, and reinforcement learning.

00:57

Lois: Hemant Gahankari, a Senior Principal OCI Instructor, joins us for this episode. Hi Hemant! Let’s dive right in. What is machine learning? How does it work?

Hemant: Machine learning is a subset of artificial intelligence that focuses on creating computer systems that can learn and predict outcomes from given examples without being explicitly programmed. It is powered by algorithms that incorporate intelligence into machines by automatically learning from a set of examples usually provided as data.

01:34

Nikita: Give us a few examples of machine learning… so we can see what it can do for us.

Hemant: Machine learning is used by all of us in our day-to-day life.

When we shop online, we get product recommendations based on our preferences and our shopping history. This is powered by machine learning.

We are notified about movies recommendations based on our viewing history and choices of other similar viewers. This too is driven by machine learning.

While browsing emails, we are warned of a spam mail because machine learning classifies whether the mail is spam or not based on its content. In the increasingly popular self-driving cars, machine learning is responsible for taking the car to its destination.

02:24

Lois: So, how does machine learning actually work?

Hemant: Let us say we have a computer and we need to teach the computer to differentiate between a cat and a dog. We do this by describing features of a cat or a dog.

Dogs and cats have distinguishing features. For example, the body color, texture, eye color are some of the defining features which can be used to differentiate a cat from a dog. These are collectively called as input data.

We also provide a corresponding output, which is called as a label, which can be a dog or a cat in this case. By describing a specific set of features, we can say that it is a cat or a dog.

Machine learning model is first trained with the data set. Training data set consists of a set of features and output labels, and is given as an input to the machine learning model.

During the process of training, machine learning model learns the relation between input features and corresponding output labels from the provided data. Once the model learns from the data, we have a trained model.

Once the model is trained, it can be used for inference. Inference is a process of getting a prediction by giving a data point. In this example, we input features of a cat or a dog, and the trained model predicts the output that is a cat or a dog label.

The types of machine learning models depend on whether we have a labeled output or not.

04:08

Nikita: Oh, there are different types of machine learning models?

Hemant: In general, there are three types of machine learning approaches.

In supervised machine learning, labeled data is used to train the model. Model learns the relation between features and labels.

Unsupervised learning is generally used to understand relationships within a data set. Labels are not used or are not available.

Reinforcement learning uses algorithms that learn from outcomes to make decisions or choices.

04:45

Lois: Ok…supervised learning, unsupervised learning, and reinforcement learning. Where do we use each of these machine learning models?

Hemant: Some of the popular applications of supervised machine learning are disease detection, weather forecasting, stock price prediction, spam detection, and credit scoring. For example, in disease detection, the patient data is input to a machine learning model, and machine learning model predicts if a patient is suffering from a disease or not.

For unsupervised machine learning, some of the most common real-time applications are to detect fraudulent transactions, customer segmentation, outlier detection, and targeted marketing campaigns. So for example, given the transaction data, we can look for patterns that lead to fraudulent transactions.

Most popular among reinforcement learning applications are automated robots, autonomous driving cars, and playing games.

05:51

Nikita: I want to get into how each type of machine learning works. Can we start with supervised learning?

Hemant: Supervised learning is a machine learning model that learns from labeled data. The model learns the mapping between the input and the output.

As a house price predictor model, we input house size in square feet and model predicts the price of a house. Suppose we need to develop a machine learning model for detecting cancer, the input to the model would be the person's medical details, the output would be whether the tumor is malignant or not.

06:29

Lois: So, that mapping between the input and output is fundamental in supervised learning.

Hemant: Supervised learning is similar to a teacher teaching student. The model is trained with the past outcomes and it learns the relationship or mapping between the input and output.

In supervised machine learning model, the outputs can be either categorical or continuous. When the output is continuous, we use regression. And when the output is categorical, we use classification.

07:05

Lois: We want to keep this discussion at a high level, so we’re not going to get into regression and classification. But if you want to learn more about these concepts and look at some demonstrations, visit mylearn.oracle.com.

Nikita: Yeah, look for the Oracle Cloud Infrastructure AI Foundations course and you’ll find a lot of resources that you can make use of.

07:30

The Oracle University Learning Community is an excellent place to collaborate and learn with Oracle experts and fellow learners. Grow your skills, inspire innovation, and celebrate your successes. All your activities, from liking a post to answering questions and sharing with others, will help you earn a valuable reputation, badges, and ranks to be recognized in the community.

Visit mylearn.oracle.com to get started.

07:58

Nikita: Welcome back! So that was supervised machine learning. What about unsupervised machine learning, Hemant?

Hemant: Unsupervised machine learning is a type of machine learning where there are no labeled outputs. The algorithm learns the patterns and relationships in the data and groups similar data items. In unsupervised machine learning, the patterns in the data are explored explicitly without being told what to look for.

For example, if you give a set of different-colored LEGO pieces to a child and ask to sort it, it may the LEGO pieces based on any patterns they observe. It could be based on same color or same size or same type. Similarly, in unsupervised learning, we group unlabeled data sets.

One more example could be-- say, imagine you have a basket of various fruits-- say, apples, bananas, and oranges-- and your task is to group these fruits based on their similarities. You observe that some fruits are round and red, while others are elongated and yellow. Without being told explicitly, you decide to group the round and red fruits together as one cluster and the elongated and yellow fruits as another cluster. There you go. You have just performed an unsupervised learning task.

09:21

Lois: Where is unsupervised machine learning used? Can you take us through some use cases?

Hemant: The first use case of unsupervised machine learning is market segmentation. In market segmentation, one example is providing the purchasing details of an online shop to a clustering algorithm. Based on the items purchased and purchasing behavior, the clustering algorithm can identify customers based on the similarity between the products purchased. For example, customers with a particular age group who buy protein diet products can be shown an advertisement of sports-related products.

The second use case is on outlier analysis. One typical example for outlier analysis is to provide credit card purchase data for clustering. Fraudulent transactions can be detected by a bank by using outliers. In some transaction, amounts are too high or recurring. It signifies an outlier.

The third use case is recommendation systems. An example for recommendation systems is to provide users' movie viewing history as input to a clustering algorithm. It clusters users based on the type or rating of movies they have watched. The output helps to provide personalized movie recommendations to users. The same applies for music recommendations also.

10:53

Lois: And finally, Hemant, let’s talk about reinforcement learning.

Hemant: Reinforcement learning is like teaching a dog new tricks. You reward it when it does something right, and over time, it learns to perform these actions to get more rewards. Reinforcement learning is a type of Machine Learning that enables an agent to learn from its interaction with the environment, while receiving feedback in the form of rewards or penalties without any labeled data.

Reinforcement learning is more prevalent in our daily lives than we might realize. The development of self-driving cars and autonomous drones rely heavily on reinforcement learning to make real time decisions based on sensor data, traffic conditions, and safety considerations.

Many video games, virtual reality experiences, and interactive entertainment use reinforcement learning to create intelligent and challenging computer-controlled opponents. The AI characters in games learn from player interactions and become more difficult to beat as the game progresses.

12:05

Nikita: Hemant, take us through some of the terminology that’s used with reinforcement learning.

Hemant: Let us say we want to train a self-driving car to drive on a road and reach its destination. For this, it would need to learn how to steer the car based on what it sees in front through a camera. Car and its intelligence to steer on the road is called as an agent.

More formally, agent is a learner or decision maker that interacts with the environment, takes actions, and learns from the feedback received. Environment, in this case, is the road and its surroundings with which the car interacts. More formally, environment is the external system with which the agent interacts. It is the world or context in which the agent operates and receives feedback for its actions.

What we see through a camera in front of a car at a moment is a state. State is a representation of the current situation or configuration of the environment at a particular time. It contains the necessary information for the agent to make decisions. The actions in this example are to drive left, or right, or keep straight. Actions are a set of possible moves or decisions that the agent can take in a given state.

Actions have an impact on the environment and influence future states. After driving through the road many times, the car learns what action to take when it views a road through the camera. This learning is a policy. Formally, policy is a strategy or mapping that the agent uses to decide which action to take in a given state. It defines the agent's behavior and determines how it selects actions.

13:52

Lois: Ok. Say we’re talking about the training loop of reinforcement learning in the context of training a dog to learn tricks. We want it to pick up a ball, roll, sit…

Hemant: Here the dog is an agent, and the place it receives training is the environment. While training the dog, you provide a positive reward signal if the dog picks it right and a warning or punishment if the dog does not pick up a trick. In due course, the dog gets trained by the positive rewards or negative punishments.

The same tactics are applied to train a machine in the reinforcement learning. For machines, the policy is the brain of our agent. It is a function that tells what actions to take when in a given state. The goal of reinforcement learning algorithm is to find a policy that will yield a lot of rewards for the agent if the agent follows that policy referred to as the optimal policy.

Through a process of learning from experiences and feedback, the agent becomes more proficient at making good decisions and accomplishing tasks. This process continues until eventually we end up with the optimal policy. The optimal policy is learned through training by using algorithms like Deep Q Learning or Q Learning.

15:19

Nikita: So through multiple training iterations, it gets better. That’s fantastic. Thanks, Hemant, for joining us today. We’ve learned so much from you.

Lois: Remember, the course and certification are free, so if you’re interested, make sure you log in to mylearn.oracle.com and get going. Join us next week for another episode of the Oracle University Podcast. Until then, I’m Lois Houston…

Nikita: And Nikita Abraham signing off!

15:48

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