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Chapters

  • Course Outline
  • Introduction to Machine Learning
  • Supervised Learning: Regression
    • Data Pre-processing
    • Dimensionality Reduction
    • PCA: Deep Dive
    • Advanced Dim Reduction (LDA, t-SNE)
    • Feature Selection
    • Regression Models
    • Model Evaluation Metrics
    • Multicollinearity & VIF
  • Supervised Learning: Classification
  • Unsupervised Learning
  • Unit 5: Trends and Applications
  • Unit 6: Advanced Topics & MLOps (Bonus)

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Machine Learning

Supervised Learning: Regression

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Supervised Learning — Regression covers the essential steps of preparing data and applying regression models to predict continuous numerical values.

Because this unit contains dense and complex concepts, we have separated the topics into their own detailed study pages. Please use the sidebar navigation or the links below to dive into each topic.

Data Pre-processing

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Dimensionality Reduction

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PCA: Deep Dive

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Advanced Dim Reduction (LDA, t-SNE)

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Feature Selection

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Regression Models

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Model Evaluation Metrics

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Multicollinearity & VIF

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Introduction to Machine LearningSupervised Learning: Classification
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