Course curriculum

  • 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
  • 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
  • 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
  • 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
  • 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
  • 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