Course curriculum
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1
Introduction
- Introduction To Instructor
- Introduction To Instructor
- Introduction to Course
- Focus of the Course
- Focus of the Course
- Packages to be Covered
- Contents to be Covered
- How to Speed up
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2
Features
- Introduction to Feature in Data Science
- Python is Important
- Marking Facial Features
- Feature Space
- Features Dimensions
- Features Dimensions- Lecture 8-Quiz
- Features Dimensions-Lecture 8-Solution
- Features Dimensions Activity
- UCI ML Repository
- Functions
- Why Dimensionality Reduction
- Activity-Dimensionality Reduction
- Feature Dimensionality Reduction Methods
- Feature Dimensionality Reduction Methods-Lecture 12- Quiz
- Feature Dimensionality Reduction Methods-Lecture 12 -Solution
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3
Feature Selection
- Why Feature Selection
- Importance of Machine Learning
- Feature Selection Methods
- Filter Methods
- Wrapper Methods
- Embedded Methods
- Search Strategy
- Search Strategy
- Forward Selection Search
- Backward Elimination Search
- Search Strategy Activity
- Statistical Based Methods
- Statistical Based Methods
- Statistical Method-Low Variance
- Statistical Method-T Score
- Statistical Method-Chi-Square Score
- Information Theoratic Methods
- Similarity Based Methods Introduction
- Similarity Based Methods Criteria
- Activity- Feature Selection in Python
- Python Implementation of Filter Method
- Python Implementation of Wrapper Method
- Python Implementation of Embedded Method
- Python Implementation of Correlation Filter
- Activity- Feature Selection
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4
Mathematical Foundation
- Introduction to Mathematical Foundation of Feature Selection
- Intro to mathematical foundation
- Closure Of A Set
- Closure Of A Set-Lecture 27-Quiz
- Closure Of A Set-Lecture 27-Solution
- Linear Combinations
- Linear Combinations- Lecture 28-Quiz
- Linear Combinations-Lecture 28-Solution
- Linear Independence
- Vector Space
- Basis and Dimensions
- Coordinates vs Dimensions
- SubSpace
- SubSpace-Lecture 33-Quiz
- SubSpace-Lecture 33-Solution
- Orthonormal Basis
- Orthonormal Basis-Lecture 34-Quiz
- Orthonormal Basis-Lecture 34-Solution
- Matrix Product
- Matrix Product-Lecture 35-Quiz
- Matrix Product-Lecture 35-Solution
- Matrix Dimensions
- Least Squares
- Linalg Library
- Rank
- Eigen Space
- Positive Semi Definite Matrix
- Singular Value Decomposition SVD
- Lagrange Multipliers
- Vector Derivatives
- Linear Algebra Module Python
- Activity-Linear Algebra Module Python
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5
Feature Extraction
- Feature Extraction Introduction
- PCA Introduction
- PCA Criteria
- PCA Properties
- PCA Max Variance Formulation
- PCA Derivation
- PCA Implementation
- PCA For Small Sample Size Problems(DualPCA)
- PCA vs SVD
- Kernel PCA
- Kernel PCA vs ISOMAP
- Kernel PCA vs The Rest
- Encoder Decoder Networks For Dimensionality Reduction vs kernel PCA
- Supervised PCA and Fishers Linear Discriminant Analysis
- Supervised PCA and Fishers Linear Discriminant Analysis Activity
- Dimensionality Reduction Pipelines Python Project
- Encoder Decoder Networks For Dimensionality Reduction vs kernel PCA
- Encoder Decoder Networks For Dimensionality Reduction vs kernel PCA
- Supervised PCA and Fishers Linear Discriminant Analysis
- Supervised PCA and Fishers Linear Discriminant Analysis
- Kernel PCA vs The Rest
- Kernel PCA vs The Rest
- Dimensionality Reduction Pipelines Python Project
- Dimensionality Reduction Pipelines Python Project
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6
Feature Engineering
- Categorical Features
- Intro to Feature Engineering
- Categorical Features Python
- Categorical Features
- Types of Encoding
- Python implementation of Encoding
- Where to use which Encoding
- Intro to missing values
- How to deal with missing values
- Python implementation of Missing Values
- Text Features
- Image Features
- Derived Features
- Derived Features Histogram Of Gradients Local Binary Patterns
- Feature Scaling
- Activity-Feature Scaling