In Part 2 of our Machine Learning series, we go beyond the basics of Supervised and Unsupervised Learning to explore advanced ML techniques that are shaping the future of Artificial Intelligence.
In this video, you’ll learn about Reinforcement Learning, Semi-Supervised Learning, and Self-Supervised Learning — how they work, where they’re used, and why they matter.
📘 What You’ll Learn:
💡 Reinforcement Learning (RL) – How AI learns by interacting with its environment through rewards and penalties.
📊 Semi-Supervised Learning – How AI combines small amounts of labeled data with large amounts of unlabeled data for better accuracy.
📈 Self-Supervised Learning – How modern AI systems train themselves without human-labeled input.
🎯 Real-World Examples:
✔ Reinforcement Learning: AI mastering chess and robotics 🤖♟️ ✔ Semi-Supervised Learning: Boosting accuracy in speech recognition 🗣️ ✔ Self-Supervised Learning: Powering AI-generated text and image classification 🧠🖼️
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