What is Normalisation?Normalisation kya hai?

🧸 Think of it likeAise samjho

Like converting marks out of 50 and out of 100 to percentages before comparing them.50 mein se aur 100 mein se marks ko compare karne se pehle percentage mein badalna.

📘 ExplainedVistaar se

For classic ML, scale features (for example to mean 0 and standard deviation 1) so large valued features don't dominate; fit the scaler on training data only. Inside neural networks, layers like BatchNorm and LayerNorm keep activations stable; transformers use LayerNorm.Classic ML mein features scale karo (jaise mean 0 aur standard deviation 1) taaki badi values wale features haavi na hon; scaler sirf training data pe fit karo. Neural networks ke andar BatchNorm aur LayerNorm jaisi layers activations stable rakhti hain; transformers LayerNorm use karte hain.

💡 Real exampleAsli example

Scaling salary (₹ lakhs) and age (years) so both matter fairly in a distance based model.Salary (₹ lakh) aur umar (saal) ko scale karna taaki distance based model mein dono barabar matter karein.

📖 Learn it step by stepStep by step seekho

• Feature engineering: turning raw data into useful inputsFeature engineering: raw data ko kaam ke inputs mein badalna
• Neural networks: layers that learnNeural networks: layers jo seekhti hain

🔗 Related termsJude hue shabd

• Feature engineeringTurning raw data into useful model inputs, often using domain knowledge.Raw data ko model ke kaam ke inputs mein badalna, aksar domain knowledge se.
• Neural networkA model made of layers of simple connected units (neurons) that learn by adjusting connection weights.Simple jude hue units (neurons) ki layers se bana model jo connection weights badal kar seekhta hai.