Core AI Concepts – Part 1 Oracle University podcast

Content provided by Oracle Universtity and Oracle Corporation. All podcast content including episodes, graphics, and podcast descriptions are uploaded and provided directly by Oracle Universtity and Oracle Corporation or their podcast platform partner. If you believe someone is using your copyrighted work without your permission, you can follow the process outlined here https://podcastplayer.com/legal.

Oracle University Podcast »
Core AI Concepts – Part 1

1d ago 20:08

MP3•Episode home

Join hosts Lois Houston and Nikita Abraham, along with Principal AI/ML Instructor Himanshu Raj, as they dive deeper into the world of artificial intelligence, analyzing the types of machine learning. They also discuss deep learning, including how it works, its applications, and its advantages and challenges. From chatbot assistants to speech-to-text systems and image recognition, they explore how deep learning is powering the tools we use today. AI for You: https://mylearn.oracle.com/ou/course/ai-for-you/152601/252500 Oracle University Learning Community: https://education.oracle.com/ou-community LinkedIn: https://www.linkedin.com/showcase/oracle-university/ X: https://x.com/Oracle_Edu Special thanks to Arijit Ghosh, David Wright, Kris-Ann Nansen, Radhika Banka, and the OU Studio Team for helping us create this episode. ------------------------------------------------------------- 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!

00:25

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, Team Lead: Editorial Services.

Nikita: Hi everyone! Last week, we went through the basics of artificial intelligence. If you missed it, I really recommend listening to that episode before you start this one. Today, we’re going to explore some foundational AI concepts, starting with machine learning. After that, we’ll discuss the two main machine learning models: supervised learning and unsupervised learning. And we’ll close with deep learning.

Lois: Himanshu Raj, our Principal AI/ML Instructor, joins us for today’s episode. Hi Himanshu! Let’s dive right in. What is machine learning?

01:12

Himanshu: Machine learning lets computers learn from examples to make decisions or predictions without being told exactly what to do. They help computers learn from past data and examples so they can spot patterns and make smart decisions just like humans do, but faster and at scale.

01:31

Nikita: Can you give us a simple analogy so we can understand this better?

Himanshu: When you train a dog to sit or fetch, you don't explain the logic behind the command. Instead, you give this dog examples and reinforce correct behavior with rewards, which could be a treat, a pat, or a praise. Over time, the dog learns to associate the command with the action and reward.

Machine learning learns in a similar way, but with data instead of dog treats. We feed a mathematical system called models with multiple examples of input and the desired output, and it learns the pattern. It's trial and error, learning from the experience. Here is another example. Recognizing faces. Humans are incredibly good at this, even as babies. We don't need someone to explain every detail of the face. We just see many faces over time and learn the patterns. Machine learning models can be trained the same way. We showed them thousands or millions of face images, each labeled, and they start to detect patterns like eyes, nose, mouth, spacing, different angles. So eventually, they can recognize faces they have seen before or even match new ones that are similar.

So machine learning doesn't have any rules, it's just learning from examples. This is the kind of learning behind things like face ID on your smartphone, security systems that recognizes employees, or even Facebook tagging people in your photos.

03:05

Lois: So, what you’re saying is, in machine learning, instead of telling the computer exactly what to do in every situation, you feed the model with data and give it examples of inputs and the correct outputs. Over time, the model figures out patterns and relationships within the data on its own, and it can make the smart guess when it sees something new.

I got it! Now let’s move on to how machine learning actually works? Can you take us through the process step by step?

Himanshu: Machine learning actually happens in three steps. First, we have the input, which is the training data. Think of this as showing the model a series of examples. It could be images of historical sales data or customer complaints, whatever we want the machine to learn from.

Next comes the pattern finding. This is the brain of the system where the model starts spotting relationships in the data. It figures out things like customer who churn or leave usually contacts support twice in the same month. It's not given rules, it just learns patterns based on the example.

And finally, we have output, which is the prediction or decision. This is the result of all this learning. Once trained, the computer or model can say this customer is likely to churn or leave. It's like having a smart assistant that makes fast, data-driven guesses without needing step by step instruction.

04:36

Nikita: What are the main elements in machine learning?

Himanshu: In machine learning, we work with two main elements, features and labels. You can think of features as the clues we provide to the model, pieces of information like age, income, or product type. And the label is the solution we want the model to predict, like whether a customer will buy or not.

04:55

Nikita: Ok, I think we need an example here. Let’s go with the one you mentioned earlier about customers who churn.

Himanshu: Imagine we have a table with data like customer age, number of visits, whether they churned or not. And each of these rows is one example. The features are age and visit count. The label is whether the customer churned, that is yes or no.

Over the time, the model might learn patterns like customer under 30 who visit only once are more likely to leave. Or frequent visitors above age 45 rarely churn. If features are the clues, then the label is the solution, and the model is the brain of the system. It's what's the machine learning builds after learning from many examples, just like we do. And again, the better the features are, the better the learning. ML is just looking for patterns in the data we give it.

05:51

Lois: Ok, we’re with you so far. Let’s talk about the different types of machine learning. What is supervised learning?

Himanshu: Supervised learning is a type of machine learning where the model learns from the input data and the correct answers. Once trained, the model can use what it learned to predict the correct answer for new, unseen inputs. Think of it like a student learning from a teacher. The teacher shows labeled examples like an apple and says, "this is an apple." The student receives feedback whether their guess was right or wrong. Over time, the student learns to recognize new apples on their own. And that's exactly how supervised learning works. It's learning from feedback using labeled data and then make predictions.

06:38

Nikita: Ok, so supervised learning means we train the model using labeled data. We already know the right answers, and we're essentially teaching the model to connect the dots between the inputs and the expected outputs. Now, can you give us a few real-world examples of supervised learning?

Himanshu: First, house price prediction. In this case, we give the model features like a square footage, location, and number of bedrooms, and the label is the actual house price. Over time, it learns how to predict prices for new homes.

The second one is email: spam or not. In this case, features might include words in the subject line, sender, or links in the email. The label is whether the email is spam or not. The model learns patterns to help us filter our inbox, as you would have seen in your Gmail inboxes.

The third one is cat versus dog classification. Here, the features are the pixels in an image, and the label tells us whether it's a cat or a dog. After seeing many examples, the model learns to tell the difference on its own.

Let's now focus on one very common form of supervised learning, that is regression. Regression is used when we want to predict a numerical value, not a category. In simple terms, it helps answer questions like, how much will it be? Or what will be the value be? For example, predicting the price of a house based on its size, location, and number of rooms. Or estimating next quarter's revenue based on marketing spend.

08:18

Lois: Are there any other types of supervised learning?

Himanshu: While regression is about predicting a number, classification is about predicting a category or type. You can think of it as the model answering is this yes or no, or which group does this belong to.

Classification is used when the goal is to predict a category or a class. Here, the model learns patterns from historical data where both the input variables, known as features, and the correct categories, called labels, are already known.

08:53

Ready to level-up your cloud skills? The 2025 Oracle Fusion Cloud Applications Certifications are here! These industry-recognized credentials validate your expertise in the latest Oracle Fusion Cloud solutions, giving you a competitive edge and helping drive real project success and customer satisfaction. Explore the certification paths, prepare with MyLearn, and position yourself for the future. Visit mylearn.oracle.com to get started today.

09:25

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

Himanshu: Unlike supervised learning, here, the model is not given any labels or correct answers. It just handed the raw input data and left to make sense of it on its own.

The model explores the data and discovers hidden patterns, groupings, or structures on its own, without being explicitly told what to look for. And it's more like a student learning from observations and making their own inferences.

09:55

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

Himanshu: The first one is product recommendation. Customers are grouped based on shared behavior even without knowing their intent. This helps show what the other users like you also prefer. Second one is anomaly detection. Unusual patterns, such as fraud, network breaches, or manufacturing defects, can stand out, all without needing thousands of labeled examples. And third one is customer segmentation. Customers can be grouped by purchase history or behavior to tailor experiences, pricing, or marketing campaigns.

10:32

Lois: And finally, we come to deep learning. What is deep learning, Himanshu?

Himanshu: Humans learn from experience by seeing patterns repeatedly. Brain learns to recognize an image by seeing it many times. The human brain contains billions of neurons. Each neuron is connected to others through synapses. Neurons communicate by passing signals. The brain adjusts connections based on repeated stimuli. Deep learning was inspired by how the brain works using artificial neurons and connections.

Just like our brains need a lot of examples to learn, so do the deep learning models. The more the layers and connections are, the more complex patterns it can learn. The brain is not hard-coded. It learns from patterns. Deep learning follows the same idea. Metaphorically speaking, a deep learning model can have over a billion neurons, more than a cat's brain, which have around 250 million neurons. Here, the neurons are mathematical units, often called nodes, or simply as units. Layers of these units are connected, mimicking how biological neurons interact.

So deep learning is a type of machine learning where the computer learns to understand complex patterns. What makes it special is that it uses neural networks with many layers, which is why we call it deep learning.

11:56

Lois: And how does deep learning work?

Himanshu: Deep learning is all about finding high-level meaning from low-level data layer by layer, much like how our brains process what we see and hear. A neural network is a system of connected artificial neurons, or nodes, that work together to learn patterns and make decisions.