Principal Component Analysis | Machine Learning Tutorial | Tutorialspoint

In this tutorial on 'Machine Learning', you will learn about Principal Component Analysis, PCA Important Terminologies, How PCA Works, Covariance Matrix Computation and more. Get Certification in AI & Machine Learning: https://www.tutorialspoint.com/certification/artificial-intelligence-and-machine-learning-certification/index.asp Use coupon ""YOUTUBE12"" to get “FLAT 12%’’ OFF at Checkout. Free Study Material: https://www.tutorialspoint.com/machine_learning/index.htm Python for Beginners 2023: https://www.youtube.com/playlist?list=PLWPirh4EWFpEV7539u8r_afMlRbt5sa6Q Quality Learning to UPSKILL yourself only at TutorialsPoint. Timestamps 0:39 Dimensionality Reduction 1:43 Why Dimensionality Reduction? 2:22 Principal Component Analysis 4:16 PCA Important Terminologies 5:03 Properties of PCA 6:14 How PCA Works? 6:30 Standardization 6:44 Covariance Matrix Computation In this video, we'll dive deep into the concepts of Principal Component Analysis and their role in programming in machine learning. We'll explain machine learning models, including their applications and implementations. Perfect for anyone attending machine learning classes or preparing for algorithm important questions. This series, you will learn all types of Machine Learning Algorithms, Supervised Learning, Unsupervised Learning, Reinforcement Learning, KNN, Decision Tree, Linear Regression, Support Vector Machine, Random Forest, Naive Bayes, Logistic regression, K means clustering, Hierarchical clustering, Anomaly detection, Q learning, Deep Q Networks, and more. Tutorialspoint, a leading ed-tech platform, offers Simply Easy Learning at affordable prices.