The Art of Artificial Intelligence: For Dummies

Clearing up AI from the basics: Supervised and Unsupervised learning

The topic of Artificial Intelligence tends to be a topic that many like to overcomplicate. In its true form, the method and understanding of Artificial Intelligence can even be understood by a third-grader.

1. What is Artificial Intelligence
2. The Magic Behind the Process
3. The Training Process
4. Supervised Learning
5. Unsupervised Learning
6. Differences Between the Learning Methods
7. When To Use Supervised Learning
8. When To Use Unsupervised Learning
9. Key Takeaways
10. What’s Next
11. A Personal Note

What Is Artificial Intelligence?

In simple terms, it’s the science of making machines more intelligent. The purpose of AI is to replicate human behavior and their learning process. With this, machines learn to mimic human behavior to execute tasks more accurately and better. Humans will never be perfect. However, machines and algorithms continue to be more and more perfect. It’s definitely not magic, so what is it?

blue: homosapiens; red: AI

The “Magic” Behind the Process

Over the years, AI is able to handle vast amounts of data in a way humans cannot. To do that, AI uses decision making, problem-solving, and critical thinking. Using strategic analysis and patterns, AI can give reports and insights for tasks. It is important to note the fact that AI’s main source of information is data, training, and prior knowledge.

The Training Process

Using complex algorithms and features that AI is equipped with, humans send data off to the machine. From there, the machine learns from data (the input), uses a model to form an understanding, and reach a conclusion (output). From there, the model gets updated with new parameters and functions. This is called the training process.

In the beginning, the AI machine will be inaccurate. But over time, it makes connections and becomes more intelligent. This is because of the training data and the changes in the algorithm/model. There are two main types of learning processes that AI uses: supervised learning and unsupervised learning.

Supervised Learning: The Basics

Oh great, another fancy word. Not to worry, you will be an expert in this in no time. Supervised learning is considered task-driven because it’s highly focused on a singular task. Supervised learning essentially learns from labeled data, trains the data using a supervised AI model, gives an output, and gets provided feedback. It’s called supervised learning because an expert moderates the algorithm. In addition, the algorithm gets the answer key. From there, the model uses to evaluate its accuracy for training data.

Unsupervised Learning: The Basics

As the name hints, unsupervised learning is not supervised by an expert. It’s data-driven because it works with unlabeled data. This means unsupervised learning is solely controlled by the unstructured data and the way it's formatted.

To do this, unsupervised learning uses data mining. Data mining is the process of discovering hidden patterns and structures in the data. Additionally, it finds similarities and differences from the data, and the machine uses its insights and extracts features to calculate the ideal output. And by ideal output, what the machine thinks is the ideal output. The output eventually becomes increasingly accurate over time.

Differences Between Supervised and Unsupervised Learning

Now you know what supervised and unsupervised learning is. They seem quite similar, so what are the key differences between the two?

• Supervised learning knows its output. It knows its end-goal, and using the expert’s feedback, it tweaks itself in a way that it slowly becomes more and more accurate to the ideal answer
• Supervised learning has a training data set. Training data is what the AI model trains on before it’s given testing data.

Unsupervised learning doesn’t know its output. It has to decide its pathway and its way of learning on its own. Computer scientists rely on unsupervised learning for answers, since most of the time the ‘expert’ doesn’t even know the answer. Eventually, it provides an accurate assumption about the data.

• Unsupervised learning doesn’t have a training data set. Essentially, the AI goes into the problem blind, relying on data exploration and data mining to reach conclusions.

organizes unlabelled data by clustering

That begs the question: when do we use supervised and unsupervised learning?

When to Use Supervised Learning

Supervised learning is all labeled data, so it makes sense to use supervised learning where data is labeled. Here are some examples:

• Classification: In other words, discrete values. An example of a classification method is checking whether tomorrow’s weather at 8 pm will be “hot” or “cold”.
• Regression: Regression is used to predict continuous values, like the temperature at 8 pm. There are many types of regression, like linear regression depending on what type of data you’re working with. That’s an article for another time.

Essentially, the goal is to make predictions.

When to Use Unsupervised Learning

Unsupervised learning starts with raw unlabeled data, so it makes sense to use unsupervised learning where data is unlabelled. Here are some examples:

• Clustering: This is the main form of use in unsupervised learning. Some examples of clustering are categorization and dividing by similarity.

For instance, you could use clustering to find the targeted audience for advertising specific ads.

Essentially, the goal is to notice pattern/structure recognition.

Key Takeaways

There’s a lot of information to unpack here. This is why I have manifested some quick takeaways:

What’s Next?

After reading this, I am sure many are interested in the in-depth details about supervised and unsupervised learning. Therefore, I have linked some of my favorite resources to start. These include AI+ML as well, though ML wasn’t talked about in this article (but look out for that in the future):

Andrew Ng’s Machine Learning Course
Intro to ML- Zero to Hero
Writing ML Games!

Hope you enjoyed reading! Check out my LinkedIn, Instagram, and follow me on Medium. I would greatly appreciate it if you could clap the story, I always put my 100% effort and dedication when writing these articles. Stay tuned and hit the follow button for weekly updates and mind-intriguing articles!