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.
Check out more Trending Courses to Learn: http://bit.ly/3KBW32w
Subscribe to our Channel to get more related updates and turn on the notification:
www.youtube.com/@TutorialsPoint_
#tutorialspoint #machinelearningalgorithm #supportvectormachines #AI #ML #machinelearning #randomforest #supervisedlearning #unsupervisedlearning #reinforcementlearning #typesofmachinelearning