#Parametres

Basic explanation of array

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PCA = Maximum variance + Minimum redundancy 👇 Orthogonal PCs Follow @ml_ka_keeda for more such videos Principal Components (PCs) are orthogonal because PCA finds directions of maximum variance that are uncorrelated with each other. Orthogonality = No redundancy. ⸻ 🔹 The Real Reason : PCA is based on the covariance matrix of the data. Key property 👉 The covariance matrix is symmetric And an important linear algebra result says: ➡️ Eigenvectors of a symmetric matrix are orthogonal In PCA: • PCs = Eigenvectors of the covariance matrix So automatically → PCs are orthogonal. ⸻ 🔹 Intuition PCA works step-by-step: Step 1: Find the direction with maximum variance → First Principal Component (PC1) Step 2: Now find the next direction with: ✔️ Maximum remaining variance ❌ No overlap with PC1 To ensure no repeated information, PCA forces PC2 to be perpendicular to PC1. Same logic continues for PC3, PC4… #datascience #linearalgebra #statistics #machinelearning #dimensionality

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My Secret revealed. There's very little "Maths". It's a computer. I let it do the math. I just tell it what when and where. It knows how many. It's an Algebra engine. You feed it the Variables and the Formula. Don't do it's job. That actually makes it so much harder for it. Because it has to process your math backwards to move on. Let it think in its own language.

Insertion Sort (The Simple Helper) 🤏: Like sorting a hand of cards—simple and stable, but slow for large data (O(n^2)). Works like a charm for small or nearly sorted arrays (O(n)). Heap Sort (The Guaranteed Performer) 🛡️: Uses Binary Heap for guaranteed O(n \log n) performance. Reliable for large data with minimal extra memory (O(1)).