- What is data science, and what are its key components?
- Explain the differences between supervised learning, unsupervised learning, and reinforcement learning.
- What is the difference between classification and regression in machine learning?
- How do you handle missing values and outliers in a dataset?
- What is feature engineering, and why is it important in machine learning?
- Explain the bias-variance tradeoff in machine learning. How do you address it?
- What is cross-validation, and why is it used in machine learning?
- How do you evaluate the performance of a machine learning model?
- What is overfitting, and how do you prevent it in machine learning?
- What are some common algorithms used in supervised learning? Provide examples.
- Explain the k-means clustering algorithm. How do you determine the optimal number of clusters?
- What is the difference between gradient descent and stochastic gradient descent?
- How do you select features for a machine learning model?
- What is the curse of dimensionality? How does it affect machine learning models?
- Explain the difference between L1 and L2 regularization in machine learning.
- What is the purpose of principal component analysis (PCA)? How do you interpret its results?
- How do you handle imbalanced datasets in machine learning?
- What are ensemble methods in machine learning? Provide examples.
- How do you interpret the results of a confusion matrix?
- What is deep learning, and how does it differ from traditional machine learning algorithms?
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