15 Important questions to ace your Machine Learning Interview with an approach to answer

15 Important questions to ace your Machine Learning Interview with an approach to answer:

1. Machine Learning Project Lifecycle:

• Define the problem
• Gather and preprocess data
• Choose a model and train it
• Evaluate model performance
• Tune and optimize the model
• Deploy and maintain the model

1. Supervised vs Unsupervised Learning:

• Supervised Learning: Uses labeled data for training (e.g., predicting house prices from features).
• Unsupervised Learning: Uses unlabeled data to find patterns or groupings (e.g., clustering customer segments).

1. Evaluation Metrics for Regression:

• Mean Absolute Error (MAE)
• Mean Squared Error (MSE)
• Root Mean Squared Error (RMSE)
• R-squared (coefficient of determination)

1. Overfitting and Prevention:

• Overfitting: Model learns the noise instead of the underlying pattern.
• Prevention: Use simpler models, cross-validation, regularization.

1. Bias-Variance Tradeoff:

• Balancing error due to bias (underfitting) and variance (overfitting) to find an optimal model complexity.

1. Cross-Validation:

• Technique to assess model performance by splitting data into multiple subsets for training and validation.

1. Feature Selection Techniques:

• Filter methods (e.g., correlation analysis)
• Wrapper methods (e.g., recursive feature elimination)
• Embedded methods (e.g., Lasso regularization)

1. Assumptions of Linear Regression:

• Linearity
• Independence of errors
• Homoscedasticity (constant variance)
• No multicollinearity

1. Regularization in Linear Models:

• Adds a penalty term to the loss function to prevent overfitting by shrinking coefficients.

1. Classification vs Regression:
   • Classification: Predicts a categorical outcome (e.g., class labels).
   • Regression: Predicts a continuous numerical outcome (e.g., house price).
2. Dimensionality Reduction Algorithms:
   • Principal Component Analysis (PCA)
   • t-Distributed Stochastic Neighbor Embedding (t-SNE)
3. Decision Tree:
   • Tree-like model where internal nodes represent features, branches represent decisions, and leaf nodes represent outcomes.
4. Ensemble Methods:
   • Combine predictions from multiple models to improve accuracy (e.g., Random Forest, Gradient Boosting).
5. Handling Missing or Corrupted Data:
   • Imputation (e.g., mean substitution)
   • Removing rows or columns with missing data
   • Using algorithms robust to missing values
6. Kernels in Support Vector Machines (SVM):
   • Linear kernel
   • Polynomial kernel
   • Radial Basis Function (RBF) kernel