Visualizing PCA Transformation | Principal Component Analysis Explained with scikit-learn скачать в хорошем качестве

Visualizing PCA Transformation | Principal Component Analysis Explained with scikit-learn

In this video, we explore Principal Component Analysis (PCA), one of the most important dimensionality reduction techniques in machine learning. 👍 Like | 💬 Comment | 🔔 Subscribe for more unsupervised Learning Videos You’ll learn how PCA works, how it aligns data with coordinate axes, and how it removes correlation between features through rotation and centering. We also visualize the PCA transformation, understand principal components, and see how PCA follows the fit/transform pattern in scikit-learn. This tutorial is perfect for beginners and intermediate learners who want a clear, intuitive understanding of PCA for supervised learning tasks like regression and classification. Topics covered in this video: What is dimensionality reduction Why PCA is important in machine learning Visualizing PCA transformation PCA de-correlation explained Principal components and variance PCA implementation using scikit-learn 💬 Follow & Connect GitHub Repository:https://github.com/dr-mushtaq/Machine... Enroll Full Course: https://coursesteach.com/ Whatsapp Group:https://chat.whatsapp.com/L9URPRThBEa... #PCA#PrincipalComponentAnalysis#MachineLearning#DimensionalityReduction#DataScience#ScikitLearn#PythonMachineLearning#PCAVisualization#MLTutorial#DataAnalysis#AI#SupervisedLearning