Demystifying Supervised Learning in AI

Demystifying Supervised Learning in AI

Table of Contents

1. Introduction
2. What is AI?
3. AI 101 Series
4. The Goal of the Series
5. Patreon Launch
6. Recap: How Does AI Work?
7. What is Supervised Learning?
8. Four Steps of Supervised Learning
9. Step 1: Getting Data
10. Step 2: Labeling Data
11. Step 3: Developing the Formula
12. Step 4: Training the Model
13. Cost Function and Model Training
14. testing the Model on New Data
15. Troubleshooting Accuracy Issues
16. Conclusion
17. Support the Channel
18. Connect with Me

🤖 AI 101: A Technical Explanation of Supervised Learning

In this episode of the AI 101 series, we will delve deeper into the concept of supervised learning, a subfield of artificial intelligence and machine learning. If you haven't seen our previous video on how AI works, I recommend checking it out for a better understanding of the fundamentals.

Introduction

Artificial intelligence (AI) has become a prominent technology in various industries, revolutionizing the way we interact with machines and perform complex tasks. But how does AI actually work? In this series, we aim to provide you with a technical explanation of different aspects related to developing and using AI.

The Goal of the Series

The AI 101 series aims to educate viewers on the various factors involved in creating and utilizing artificial intelligence. We will explore topics such as supervised learning, unsupervised learning, neural networks, and more. These videos will be released on a recurring basis, interspersed with our regular content on AI in everyday life.

Patreon Launch

Before we dive into the technical details, I want to let you know that my Patreon has officially launched. By becoming a patron, you can support me in expanding the types and quality of content on this channel. My ultimate goal is to keep the channel ad-free once I become eligible for monetization. So if you have a few dollars to spare and would like to contribute, check out patreon.com/everydayai.

Recap: How Does AI Work?

In our previous video, we discussed the basics of AI, specifically deep learning. We explored how AI systems use thousands of computational neurons to develop a non-linear formula that relates input variables to output variables. This video focused on supervised learning, which involves using labeled data to train a model.

What is Supervised Learning?

Supervised learning is a task in which we aim to relate a set of input variables to a set of output variables. We need labeled data, where each sample is assigned a specific class or category, to train our model effectively. For instance, if we want to predict movie genres or types of food, we need data labeled with the corresponding classes.

Four Steps of Supervised Learning

Supervised learning can be divided into four main steps: getting data, labeling data, developing the formula, and using the formula on new data. Let's take a closer look at each step.

Step 1: Getting Data

The first step in any supervised learning task is collecting the Relevant data. This could be in the form of images, 3D data, or other types of information specific to your problem. It's crucial to identify the type of dataset you require before proceeding to the next step.

Step 2: Labeling Data

Once you have the data, you need to label it according to the desired output. For simple cases, you may assign a single label to each sample. However, for niche datasets like medical data, where expert analysis is necessary, labeling can be more complex. Medical imaging or disease diagnosis requires specialized knowledge for accurate labeling.

Step 3: Developing the Formula

After labeling the data, the next step is to develop a formula that relates the input variables to the assigned labels. This process is known as model selection and can range from simple techniques like linear or logistic regression to more complex ones like neural networks.

Step 4: Training the Model

Training the model involves making an initial guess at the formula, testing it on the labeled data, and adjusting it to minimize errors. Typically, models utilize a cost function to quantify the deviation between predicted and actual labels. The goal is to find the formula that minimizes this cost function.

Cost Function and Model Training

A cost function helps evaluate the accuracy of the model by penalizing wrong predictions. Different algorithms use various cost functions, depending on the problem at HAND. For example, in medical applications, where false negatives can have severe consequences, models may prioritize penalty on certain types of errors.

Testing the Model on New Data

Once the model's accuracy on the training data is satisfactory, it's time to evaluate its performance on new, unseen data. A separate test dataset, often comprising 20% of the collected data, is used for this purpose. If the accuracy on the test data is similar to the training data, it indicates a reliable formula. Significant discrepancies may require reconfiguration of the model or data.

Troubleshooting Accuracy Issues

While high accuracy can be achieved on the training data, generalization to new data can sometimes pose challenges. When the model performs poorly on test data, it may indicate overfitting, where the model becomes too specific to the training data. Troubleshooting such issues requires careful analysis and adjustments to improve the model's performance.

Conclusion

In this first episode of AI 101, we explored the concept of supervised learning, one of the fundamental techniques in artificial intelligence. We discussed the four steps involved in supervised learning: obtaining and labeling data, developing the formula, and training the model. Testing the model's accuracy on new data and troubleshooting performance issues were also covered.

Support the Channel

If you found this technical take on AI interesting, show your support by smashing that like button and subscribing to the channel. Your feedback and engagement are valuable to me. Consider becoming a patron on Patreon to gain exclusive insights into upcoming topics for the AI 101 series.

Connect with Me

For more updates and to join the AI community, you can find me on Twitter. Stay tuned for the next episode of AI 101, where we will explore another fascinating aspect of artificial intelligence. See you soon!

Highlights

• The AI 101 series aims to provide a technical explanation of various aspects of artificial intelligence.
• Supervised learning involves relating input variables to output variables using labeled data.
• Four steps of supervised learning: getting data, labeling data, developing the formula, and training the model.
• Cost function helps evaluate the accuracy of the model, and testing on new data ensures generalization.
• Overfitting can be a challenge in supervised learning, requiring adjustments to improve performance.

FAQs

Q: What is the AI 101 series about? A: The AI 101 series provides a technical explanation of different factors involved in developing and using artificial intelligence.

Q: How does supervised learning work? A: Supervised learning involves relating input variables to output variables using labeled data. The goal is to develop a formula that accurately predicts the class or category of new data.

Q: What are the four steps of supervised learning? A: The four steps are getting the data, labeling the data, developing the formula, and training the model. These steps help create a reliable predictive model.

Q: How can I support the channel and the AI 101 series? A: You can support the channel by liking the videos, subscribing, and becoming a patron on Patreon. Your contribution helps in expanding the content and maintaining an ad-free experience.

Resources:

• Patreon: Everyday AI
• Twitter: @everydayai