![]() The sum of the variance of the new features / the principal components should be equal to the sum of the variance of the original features.Hence, the first principal component should capture the maximum variability, the second one should capture the next highest variability etc. The principal components are generated in order of the variability in the data that it captures.the covariance between the new features (in case of PCA, they are the principal components) is 0. Removing stop words with NLTK in Python.Regression and Classification | Supervised Machine Learning.Basic Concept of Classification (Data Mining).Gradient Descent algorithm and its variants.ML | Momentum-based Gradient Optimizer introduction.Optimization techniques for Gradient Descent.ML | Mini-Batch Gradient Descent with Python.Difference between Batch Gradient Descent and Stochastic Gradient Descent.Difference between Gradient descent and Normal equation.ML | Normal Equation in Linear Regression.Mathematical explanation for Linear Regression working.Linear Regression (Python Implementation).Classifying data using Support Vector Machines(SVMs) in Python.ML | Naive Bayes Scratch Implementation using Python.Architecture and Learning process in neural network.ISRO CS Syllabus for Scientist/Engineer Exam.ISRO CS Original Papers and Official Keys.GATE CS Original Papers and Official Keys. ![]()
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