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Dimensionality Reduction technique in machine learning both theory and code in Python. Includes topics from PCA, LDA, Kernel PCA, Factor Analysis and t-SNE algorithm

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Dimensionality-Reduction-in-Python

  • Problem of Multicollinearity, lead to Overfitting
  • Dimesionality reduction reduces dimension and not loose any information

01 Curse of Dimensionality (Theory)

  • Risk of Overfitting

02 Correlation (Theory)

  • Strength and Relationship between two variables
    1. Spearman Correlation (Assume linear relation between variable)
    2. Pearson Correlation (Not assumne any kind of relationship)

03 Collinearity & Multi Collinearity (Theory)

  • Definition, Why it is a problem ?
  • Technique to check collinearity : VIF

04 Variation Inflation Factor (VIF) (Theory)

  • Deteces Multicollinearity in the dataset

05 Dimensionality Reduction Overview (Theory)

  • Problem of Multicollinearity, lead to Overfitting
  • Dimesionality reduction reduces dimension and not loose any information
  • Definition, Type of Dimensionality Reduction Technique
    1. PCA
    2. Factor Analysis
    3. LDA
    4. T-sne

06 LDA (Theory)

  • Linear Discriminant Analysis
  • Used for Supervised Classification problem

07 LDA (Python Code)

  • Steps by steps to solve LDA

08 PCA (Theory)

  • Principal Component Analysis
  • Used for Unsupervised Learning problem

09 PCA Example (Theory)

  • Example of PCA in depth

10 PCA Practical Tips (Theory)

  • Different Practical Tips to solve before PCA
    1. Variable are on Same Scale
    2. How many principal component should be expect

11 Eigen Value and Eigen Vector (Theory)

  • Definition and Example

12 PCA (Python Code)

  • Steps by steps to solve PCA

13 Dimensionality Reduction Assumptions (Theory)

  • Assumption of Each Dimensionality Reduction Algorithim

14 Factor Analysis (Theory)

  • Definition, Steps and Example

15 Factor Analysis (Python Code)

  • Steps by steps to solve Factor Analysis

16 Interview Question Dimensionality Reduction

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Dimensionality Reduction technique in machine learning both theory and code in Python. Includes topics from PCA, LDA, Kernel PCA, Factor Analysis and t-SNE algorithm

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