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.
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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.
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