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Feature Engineering and Dimensionality Reduction with Python
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[TutsNode.com] - Feature Engineering and Dimensionality Reduction with Python (Size: 3.59 GB) (Files: 206)
[TutsNode.com] - 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
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
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