RE: LeoThread 2024-09-03 08:38

[taskmaster4450le]

1. Probability Theory: Understanding probability distributions, Bayes' theorem, and conditional probability.
2. Optimization: Minimizing or maximizing a loss function to train models.
3. Overfitting and Underfitting: Understanding how to avoid these common pitfalls in ML.
4. Regularization: Techniques to prevent overfitting, such as L1 and L2 regularization.
5. Evaluation Metrics: Understanding how to measure the performance of ML models, such as accuracy, precision, recall, and F1-score.