Скачать или смотреть How Machine Learning Learns | Gradient Descent Explained Simply

Have you ever wondered how Machine Learning actually learns?

In this video, I explain *how Machine Learning trains itself* using *Optimization and Gradient Descent* — in a simple, intuitive, beginner-friendly way.

No heavy math.
No boring theory.
Just clear understanding.

👉 By the end of this video, you’ll know EXACTLY how ML models improve step by step.

📌 What you’ll learn in this lecture:
• What optimization really means in machine learning
• What a loss function is (with intuition)
• Why models start with bad predictions
• What gradients actually represent
• How Gradient Descent works step by step
• Learning rate explained (too small vs too large)
• Batch vs Stochastic vs Mini-Batch Gradient Descent
• Local vs global minima (easy intuition)
• How neural networks are trained
• Common beginner mistakes in ML training

This video is perfect for:
✔ Machine Learning beginners
✔ Data Science students
✔ Engineering & CS students
✔ Anyone confused about how ML learns

⏱️ TIMESTAMPS:
00:00 How machine learning learns
02:15 Why optimization is needed
05:10 Loss functions explained
09:40 Gradients & intuition
14:20 Gradient Descent explained
20:30 Learning rate problems
25:40 Types of Gradient Descent
31:20 Optimization challenges
36:10 Practical ML example
41:20 Summary & next lecture

📚 NEXT LECTURE:
➡ Regularization & Overfitting (How ML Avoids Overfitting)

👍 If this video helped you, LIKE & SUBSCRIBE.
💬 Comment *“Regularization”* to get the next lecture.