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Day 30 of my 100 Days of Tech series is live!
Today’s topic dives into the three core types of Machine Learning — the practical ways machines learn from data and make predictions.

Machine Learning is not one thing — it’s a set of techniques that power everything from recommendation systems to autonomous agents. Understanding the differences between Supervised, Unsupervised, and Reinforcement Learning gives you a clear mental model of how AI systems are built and applied in the real world.

🔎 What you’ll learn:

Supervised Learning — Learning from labeled examples

Unsupervised Learning — Discovering patterns without preexisting labels

Reinforcement Learning — Learning through trial and reward

When to use which approach

Real-world examples you encounter daily

Explore this topic in the format you prefer 👇

📘 Read the Medium article:
  / 5-surprising-truths-about-how-ai-actually-...  

🎧 Listen to the Spotify Podcast:
https://open.spotify.com/episode/0y6m...

📺 Watch the Video Explainer:
   • Day 30: Types of Machine Learning Explaine...  

Whether you’re a developer, data enthusiast, or tech leader, this breakdown gives you the foundational clarity needed to understand how modern AI systems are trained, tuned, and deployed.

💬 Comment “ML Day 30” if you’re following the series or want to share your own insights!

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