ALL ABOUT AI

📰 Medium · Deep Learning

Learn the basics of supervised and unsupervised learning in AI and how they work, with examples and analogies to help understand the concepts.

beginner Published 18 Jun 2026
Action Steps
  1. Learn about supervised learning and its application in classification and regression tasks using labelled data.
  2. Understand how supervised learning works by making predictions, comparing them to correct answers, and adjusting the model.
  3. Explore unsupervised learning and its use with unlabeled data to discover patterns and relationships.
  4. Apply supervised learning to a task, such as image classification, using a library like TensorFlow or PyTorch.
  5. Experiment with unsupervised learning using a dataset like MNIST or Iris to identify clusters or dimensions.
Who Needs to Know This

Data scientists and machine learning engineers can benefit from understanding these fundamental concepts to build and train accurate models. AI engineers can also apply these concepts to develop intelligent systems.

Key Insight

💡 Supervised learning uses labelled data to train models, while unsupervised learning uses unlabeled data to discover patterns and relationships.

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🤖 Learn about supervised and unsupervised learning in AI! Understand how they work and apply them to build intelligent models. #AI #MachineLearning

Key Takeaways

Learn the basics of supervised and unsupervised learning in AI and how they work, with examples and analogies to help understand the concepts.

Full Article

Title: ALL ABOUT AI

URL Source: https://medium.com/@brokenversion11/all-about-ai-d2298e9b5e89?source=rss------deep_learning-5

Published Time: 2026-06-18T03:26:35Z

Markdown Content:
# ALL ABOUT AI. 1. Supervised Learning (The Guided… | by Brokenversion | Jun, 2026 | Medium

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# ALL ABOUT AI

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## 1. Supervised Learning (The Guided Learner)

In supervised learning, the AI learns from **labelled data**. This means for every input, you already know the correct output. You are essentially giving the model an answer key.

## **How it works:** The model makes a prediction, compares it to the correct answer, and adjusts itself until it gets it right.

* **Analogy:** A student studying with a teacher who corrects their practice tests.
* **Common Tasks:**
* **Classification:** Predicting a category (e.g., Is this email “Spam” or “Not Spam”?).
* **Regression:** Predicting a continuous number (e.g., What will the price of a house be based on its size?).

## 2. Unsupervised Learning (The Self-Discoverer)

In unsupervised learning, the AI is given **unlabeled data**.
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