Feature Engineering and Dimensionality Reduction with Python

/.../03 Feature Selection/

024 Activity- Feature Selection in Python.mp4

024 Activity- Feature Selection in Python.en.srt

020 Statistical Based Methods.en.srt

018 Search Strategy.en.srt

023 Similarity Based Methods Criteria.en.srt

021 Information Theoratic Methods.en.srt

016 Wrapper Methods.en.srt

017 Embedded Methods.en.srt

019 Search Strategy Activity.en.srt

022 Similarity Based Methods Introduction.en.srt

013 Why Feature Selection.en.srt

025 Activity- Feature Selection.en.srt

015 Filter Methods.en.srt

014 Feature Selection Methods.en.srt

020 Statistical Based Methods.mp4

023 Similarity Based Methods Criteria.mp4

018 Search Strategy.mp4

021 Information Theoratic Methods.mp4

022 Similarity Based Methods Introduction.mp4

016 Wrapper Methods.mp4

017 Embedded Methods.mp4

013 Why Feature Selection.mp4

015 Filter Methods.mp4

014 Feature Selection Methods.mp4

019 Search Strategy Activity.mp4

025 Activity- Feature Selection.mp4

/

TutsNode.com.txt

[TGx]Downloaded from torrentgalaxy.to .txt

/.../05 Feature Extraction/

060 Dimensionality Reduction Pipelines Python Project.en.srt

051 PCA Implementation.en.srt

056 Kernel PCA vs The Rest.en.srt

057 Encoder Decoder Networks For Dimensionality Reduction vs kernel PCA.en.srt

058 Supervised PCA and Fishers Linear Discriminant Analysis.en.srt

055 Kernel PCA vs ISOMAP.en.srt

050 PCA Derivation.en.srt

049 PCA Max Variance Formulation.en.srt

053 PCA vs SVD.en.srt

054 Kernel PCA.en.srt

052 PCA For Small Sample Size Problems(DualPCA).en.srt

047 PCA Criteria.en.srt

060 Dimensionality Reduction Pipelines Python Project.mp4

048 PCA Properties.en.srt

046 PCA Introduction.en.srt

045 Feature Extraction Introduction.en.srt

059 Supervised PCA and Fishers Linear Discriminant Analysis Activity.en.srt

051 PCA Implementation.mp4

055 Kernel PCA vs ISOMAP.mp4

056 Kernel PCA vs The Rest.mp4

058 Supervised PCA and Fishers Linear Discriminant Analysis.mp4

057 Encoder Decoder Networks For Dimensionality Reduction vs kernel PCA.mp4

047 PCA Criteria.mp4

053 PCA vs SVD.mp4

052 PCA For Small Sample Size Problems(DualPCA).mp4

050 PCA Derivation.mp4

046 PCA Introduction.mp4

054 Kernel PCA.mp4

049 PCA Max Variance Formulation.mp4

048 PCA Properties.mp4

045 Feature Extraction Introduction.mp4

059 Supervised PCA and Fishers Linear Discriminant Analysis Activity.mp4

/01 Introduction/

003 Request for Your Honest Review.en.srt

004 Link to Github to get the Python Notebooks.html

001 Introduction To Instructor.en.srt

002 Focus of the Course.en.srt

001 Introduction To Instructor.mp4

002 Focus of the Course.mp4

003 Request for Your Honest Review.mp4

/.../04 Mathematical Foundation/

043 Linear Algebra Module Python.en.srt

032 Coordinates vs Dimensions.en.srt

033 SubSpace.en.srt

030 Vector Space.en.srt

027 Closure Of A Set.en.srt

035 Matrix Product.en.srt

040 Singular Value Decomposition SVD.en.srt

028 Linear Combinations.en.srt

031 Basis and Dimensions.en.srt

039 Positive Semi Definite Matrix.en.srt

038 Eigen Space.en.srt

037 Rank.en.srt

034 Orthonormal Basis.en.srt

036 Least Squares.en.srt

041 Lagrange Multipliers.en.srt

029 Linear Independence.en.srt

042 Vector Derivatives.en.srt

026 Introduction to Mathematical Foundation of Feature Selection.en.srt

044 Activity-Linear Algebra Module Python.en.srt

032 Coordinates vs Dimensions.mp4

033 SubSpace.mp4

030 Vector Space.mp4

043 Linear Algebra Module Python.mp4

040 Singular Value Decomposition SVD.mp4

031 Basis and Dimensions.mp4

035 Matrix Product.mp4

039 Positive Semi Definite Matrix.mp4

028 Linear Combinations.mp4

027 Closure Of A Set.mp4

038 Eigen Space.mp4

036 Least Squares.mp4

037 Rank.mp4

042 Vector Derivatives.mp4

034 Orthonormal Basis.mp4

041 Lagrange Multipliers.mp4

029 Linear Independence.mp4

026 Introduction to Mathematical Foundation of Feature Selection.mp4

044 Activity-Linear Algebra Module Python.mp4

/.../02 Features in Data Science/

010 Why Dimensionality Reduction.en.srt

011 Activity-Dimensionality Reduction.en.srt

012 Feature Dimensionality Reduction Methods.en.srt

007 Feature Space.en.srt

008 Features Dimensions.en.srt

005 Introduction to Feature in Data Science.en.srt

006 Marking Facial Features.en.srt

009 Features Dimensions Activity.en.srt

010 Why Dimensionality Reduction.mp4

007 Feature Space.mp4

008 Features Dimensions.mp4

012 Feature Dimensionality Reduction Methods.mp4

005 Introduction to Feature in Data Science.mp4

006 Marking Facial Features.mp4

009 Features Dimensions Activity.mp4

011 Activity-Dimensionality Reduction.mp4

/.../06 Feature Engineering/

064 Image Features.en.srt

063 Text Features.en.srt

065 Derived Features.en.srt

066 Derived Features Histogram Of Gradients Local Binary Patterns.en.srt

061 Categorical Features.en.srt

062 Categorical Features Python.en.srt

067 Feature Scaling.en.srt

068 Activity-Feature Scaling.en.srt

069 THANK YOU Bonus Video.en.srt

063 Text Features.mp4

066 Derived Features Histogram Of Gradients Local Binary Patterns.mp4

064 Image Features.mp4

065 Derived Features.mp4

062 Categorical Features Python.mp4

069 THANK YOU Bonus Video.mp4

061 Categorical Features.mp4

067 Feature Scaling.mp4

068 Activity-Feature Scaling.mp4

/.pad/

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