Data science interview questions?

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?

MARKETING SERVICES