PCA on MNIST + SVM | C, Gamma & Kernel Effect on Decision Boundary | Urdu / Hindi

This is Python Programming Lecture 74. In this lecture, we apply PCA (Principal Component Analysis) on the MNIST dataset and then use Support Vector Machine (SVM) for classification. We explore how PCA can reduce the dimensionality of the MNIST data and make it easier to work with high-dimensional image features. After PCA, we dive into Support Vector Machines and understand how important hyperparameters such as C, Gamma, and Kernel affect the model and its decision boundary. We discuss how changing C can influence the trade-off between a wider margin and classification errors, while Gamma controls how strongly individual training points influence the decision boundary. We also explore different kernel functions and understand how the choice of kernel changes the shape and complexity of the decision boundary. The concepts are explained using practical examples and visualizations in Urdu, making this lecture useful for understanding SVM beyond simply using the default parameters. 📌 Topics Covered ✅ PCA on MNIST Dataset ✅ MNIST Dataset ✅ Dimensionality Reduction with PCA ✅ Support Vector Machine (SVM) ✅ SVM Classification ✅ Decision Boundary ✅ SVM Hyperparameters ✅ C Parameter in SVM ✅ Gamma Parameter in SVM ✅ Kernel in SVM ✅ Linear Kernel ✅ Non-Linear Kernels ✅ Effect of C on Decision Boundary ✅ Effect of Gamma on Decision Boundary ✅ Effect of Kernel on Decision Boundary ✅ Practical SVM Visualization 🎯 What You Will Learn ✔ Understand how PCA can be applied to the MNIST dataset ✔ Learn why dimensionality reduction is useful for high-dimensional data ✔ Understand the basic working of Support Vector Machines ✔ Understand what a decision boundary is ✔ Learn how C affects the SVM decision boundary ✔ Understand how Gamma controls the influence of training points ✔ Learn how different kernels create different decision boundaries ✔ Understand the difference between linear and non-linear decision boundaries ✔ Develop intuition for tuning SVM hyperparameters ✔ Apply PCA and SVM together in a practical Machine Learning problem 📚 Urdu Explanation + Practical Coding This lecture explains PCA, MNIST, and Support Vector Machine in simple Urdu, with practical coding and visualizations to understand how C, Gamma, and Kernel change the SVM decision boundary. 👍 Like the video if you found it helpful 📤 Share it with other Machine Learning learners 🔔 Subscribe for more Python, Machine Learning, Deep Learning, AI & Data Science projects in Urdu #pythonforbeginners #pythonprogramming #learnpythonprogramming #learnpythonprogramming #pythonforaiandds #pythonprogrammingforbeginners #learnpython #pythonconcepts #pythonconcept #pythontutorial