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Professor Dave Explains
Orthogonality and Orthonormality
Defining vectors as being orthogonal and orthonormal.
Curated Video
Data Science and Machine Learning (Theory and Projects) A to Z - Feature Extraction: PCA Criteria
In this video, we will cover PCA criteria.
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This clip is from the chapter "Machine Learning: Feature Engineering and Dimensionality Reduction with Python" of the series "Data Science and Machine Learning (Theory and Projects)...
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This clip is from the chapter "Machine Learning: Feature Engineering and Dimensionality Reduction with Python" of the series "Data Science and Machine Learning (Theory and Projects)...
Curated Video
Data Science and Machine Learning (Theory and Projects) A to Z - Feature Extraction: PCA Introduction
In this video, we will cover PCA introduction.
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This clip is from the chapter "Machine Learning: Feature Engineering and Dimensionality Reduction with Python" of the series "Data Science and Machine Learning (Theory and...
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This clip is from the chapter "Machine Learning: Feature Engineering and Dimensionality Reduction with Python" of the series "Data Science and Machine Learning (Theory and...
Curated Video
Data Science and Machine Learning (Theory and Projects) A to Z - Feature Extraction: PCA Properties
In this video, we will cover PCA properties.
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This clip is from the chapter "Machine Learning: Feature Engineering and Dimensionality Reduction with Python" of the series "Data Science and Machine Learning (Theory and...
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This clip is from the chapter "Machine Learning: Feature Engineering and Dimensionality Reduction with Python" of the series "Data Science and Machine Learning (Theory and...
Curated Video
Data Science and Machine Learning (Theory and Projects) A to Z - Feature Extraction: PCA Max Variance Formulation
In this video, we will cover PCA Max Variance Formulation.
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This clip is from the chapter "Machine Learning: Feature Engineering and Dimensionality Reduction with Python" of the series "Data Science and Machine Learning...
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This clip is from the chapter "Machine Learning: Feature Engineering and Dimensionality Reduction with Python" of the series "Data Science and Machine Learning...
Curated Video
Data Science and Machine Learning (Theory and Projects) A to Z - Feature Extraction: Encoder Decoder Networks for Dimensionality Reduction Versus Kernel PCA
In this video, we will cover encoder decoder networks for dimensionality reduction versus Kernel PCA.
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This clip is from the chapter "Machine Learning: Feature Engineering and Dimensionality Reduction with Python" of the...
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This clip is from the chapter "Machine Learning: Feature Engineering and Dimensionality Reduction with Python" of the...
Curated Video
Data Science and Machine Learning (Theory and Projects) A to Z - Mathematical Foundation: Introduction to Mathematical Foundation of Feature Selection
In this video, we will cover an introduction to mathematical foundation of feature selection.
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This clip is from the chapter "Machine Learning: Feature Engineering and Dimensionality Reduction with Python" of the series "Data...
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This clip is from the chapter "Machine Learning: Feature Engineering and Dimensionality Reduction with Python" of the series "Data...
Curated Video
Data Science and Machine Learning (Theory and Projects) A to Z - Mathematical Foundation: Eigen Space
In this video, we will cover Eigen Space.
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This clip is from the chapter "Machine Learning: Feature Engineering and Dimensionality Reduction with Python" of the series "Data Science and Machine Learning (Theory and Projects) A...
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This clip is from the chapter "Machine Learning: Feature Engineering and Dimensionality Reduction with Python" of the series "Data Science and Machine Learning (Theory and Projects) A...
Khan Academy
Khan Academy: Linear Algebra: Introduction to the Null Space of a Matrix
Video first reviews the criteria for a subspace and then goes through the steps to show the null space meets the criteria and is a a subspace. This video also appears in the strand Algebra: Matrices. [10:21]
Khan Academy
Khan Academy: Linear Algebra: Linear Subspaces
Video defines a vector subspace by its three criteria: containing the zero vector, closure under scalar multiplication and closure under vector addition. Gives examples of vector sets and testing to see if they are vector subspaces. Then...
Khan Academy
Khan Academy: Linear Algebra: Im(t): Image of a Transformation
Video first reviews the closure properties of subspaces. Shows that the image of a subspace under a linear transformation is a subspace. Connects the meaning of range to the image of the transformation. Shows that the image of the linear...
Khan Academy
Khan Academy: Linear Algebra: Im(t): Image of a Transformation
Video first reviews the closure properties of subspaces. Shows that the image of a subspace under a linear transformation is a subspace. Connects the meaning of range to the image of the transformation. Shows that the image of the linear...
Khan Academy
Khan Academy: Representing Vectors in Rn Using Subspace Members
This video shows that any member of Rn can be represented as a unique sum of a vector in subspace V and a vector in the orthogonal complement of V.
Khan Academy
Khan Academy: Linear Algebra: Linear Subspaces
Video defines a vector subspace by its three criteria: containing the zero vector, closure under scalar multiplication and closure under vector addition. Gives examples of vector sets and testing to see if they are vector subspaces. Then...
Khan Academy
Khan Academy: Null Space and Column Space: Column Space of a Matrix
A video introducing the column space of a matrix.
Khan Academy
Khan Academy: Orthogonal Complements: Dim(v) + Dim(orthogonal Complement of V)=n
Showing that if V is a subspace of Rn, then dim(V) + dim(V's orthogonal complement) = n