```json
{
  "topic": "Linear Regression",
  "expertise_level": "Beginner to Intermediate",
  "additional_info": "Focus on understanding the basics of linear regression, its applications, and how to implement it using common programming languages or statistical software.",
  "subtopics": [
    "Introduction to Linear Regression: Definition and Purpose",
    "Types of Linear Regression: Simple vs. Multiple Linear Regression",
    "Assumptions of Linear Regression",
    "Cost Function: Understanding Mean Squared Error (MSE)",
    "Ordinary Least Squares (OLS) Method",
    "Gradient Descent Algorithm",
    "Evaluating Model Performance: R-squared, Adjusted R-squared, RMSE",
    "Bias-Variance Tradeoff",
    "Regularization Techniques: Ridge, Lasso, and Elastic Net",
    "Feature Engineering for Linear Regression",
    "Polynomial Regression",
    "Handling Categorical Variables",
    "Linear Regression in Python with scikit-learn",
    "Linear Regression in R",
    "Model Interpretation and Insights",
    "Applications of Linear Regression in various fields",
    "Diagnosing and Addressing Multicollinearity",
    "Outlier Detection and Handling",
    "Cross-Validation Techniques",
    "Beyond Linearity: Introduction to Non-linear Models"
  ]
}
```