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12.31 K-S Test for similarity of two distributions
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12.32 Code Snippet K-S Test
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12.33 Hypothesis testing: another example
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12.34 Resampling and Permutation test: another example
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12.35 How to use hypothesis testing?
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12.36 Proportional Sampling
#Module 14 : Dimensionality reduction and Visualization
Section 14 is divided into sub sections
π 14.1 What is Dimensionality reduction?
π 14.2 Row Vector and Column Vector
π 14.3 How to represent a data set?
π 14.4 How to represent a dataset as a Matrix.
π 14.5 Data Preprocessing: Feature Normalisation
π 14.6 Mean of a data matrix
π 14.7 Data Preprocessing: Column Standardization
π 14.8 Co-variance of a Data Matrix
π 14.9 MNIST dataset (784 dimensional)
π 14.10 Code to Load MNIST Data Set
Section 14 is divided into sub sections
π 14.1 What is Dimensionality reduction?
π 14.2 Row Vector and Column Vector
π 14.3 How to represent a data set?
π 14.4 How to represent a dataset as a Matrix.
π 14.5 Data Preprocessing: Feature Normalisation
π 14.6 Mean of a data matrix
π 14.7 Data Preprocessing: Column Standardization
π 14.8 Co-variance of a Data Matrix
π 14.9 MNIST dataset (784 dimensional)
π 14.10 Code to Load MNIST Data Set
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14.1 What is dimensionality reduction?
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14.3 How to represent a data set?
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14.4 - How to represent a dataset as a Matrix.
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14.6 - Mean of a data matrix
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14.8 - Co-variance of a Data Matrix
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14.10 - Code to Load MNIST Data Set