Machine Learning Interview Questions & Answers | Machine Learning Interview Preparation |Simplilearn

Introduction

Hello everyone, welcome to this session of Machine Learning Interview Preparation. I'm Mohan from Simplilearn, and today we'll talk about interview questions for machine learning. This article consolidates 30 commonly asked questions and provides generic answers to help you prepare. Supplement these responses with your own practical experience for a strong impact.

1. Types of Machine Learning

One of the first questions you may face is regarding the types of machine learning.

• Supervised Learning: Requires labeled data.
• Unsupervised Learning: Does not use labeled data.
• Reinforcement Learning: Involves an agent operating within an environment and learning through rewards and punishments.

2. Overfitting

Overfitting occurs when a model memorizes the training data but performs poorly on new data. It's like a child memorizing fruits but unable to recognize new fruits. Overfitting can be avoided using techniques like regularization.

3. Training and Test Sets

When training a machine learning model, data is split into training and test sets. The split ratio is flexible, commonly used ratios include 70:30 or 80:20. This helps to test the model with unseen data.

4. Handling Missing Data

Handling missing or corrupt data varies with scenarios. Common techniques include:

• Removing records with missing data.
• Filling missing values with mean, minimum, or other significant values.

5. Choosing Classifier Based on Data Size

Choosing a classifier is not directly based on data size. It involves testing multiple classifiers to identify the best-performing one.

6. Confusion Matrix

A confusion matrix helps measure the performance of a classification algorithm:

• True Positive (TP)
• False Positive (FP)
• False Negative (FN)
• True Negative (TN)

Accuracy can be calculated using the formula:

[ \text(Accuracy) = \frac(TP + TN)(TP + TN + FP + FN) ]

7. False Positive and False Negative

• False Positive (FP): Predicted positive but actually negative.
• False Negative (FN): Predicted negative but actually positive.

8. Machine Learning Process

Steps involved in the machine learning process include:

1. Data Collection & Preprocessing
2. Model Selection
3. Training & Testing the Model
4. Model Deployment

9. Deep Learning

Deep Learning is a subset of machine learning using neural networks. It automates feature engineering and works with large datasets on high-end systems.

10. Uses of Machine Learning

Applications of supervised machine learning include:

• Email spam detection
• Healthcare diagnostics

11. Semi-Supervised Learning

Semi-Supervised Learning is used when some data is labeled and some is not. It helps when labeling the entire dataset is impractical.

12. Unsupervised Learning Techniques

Clustering and association are common techniques.

13. Supervised vs. Unsupervised Learning

Supervised learning uses labeled data, while unsupervised learning does not.

14. Inductive vs. Deductive Learning

Inductive Learning: Learning through provided information (e.g., videos). Deductive Learning: Learning through experience.

15. KNN vs K-Means

KNN is a classification algorithm (supervised learning), whereas K-Means is a clustering algorithm (unsupervised learning).

16. Naive Bayes Classifier

The Naive Bayes classifier assumes that features are independent and unrelated.

17. Reinforcement Learning Examples

Examples include:

• Self-driving cars
• Chess or Go gaming systems

18. Algorithm Selection

Choosing an algorithm involves trial and error, focusing on performance metrics like accuracy.

19. Bias and Variance

• Bias: Predicted values are consistently far from actual values.
• Variance: Predicted values vary widely between different data points.

20. Trade-off between Bias and Variance

Balancing bias and variance ensures consistent and accurate predictions.

21. Precision and Recall

[ \text(Precision) = \frac(TP)(TP + FP) ] [ \text(Recall) = \frac(TP)(TP + FN) ]

22. Decision Tree Pruning

Decision Tree Pruning reduces overfitting by minimizing the number of nodes.

23. Logistic Regression

Logistic Regression is used for binary classification based on calculated probabilities.

24. K-Nearest Neighbor (KNN)

KNN identifies the class of a data point based on the majority class of its 'k' nearest neighbors.

Conclusion

Practicing these questions and understanding their concepts will enhance your preparation for machine learning interviews. Be prepared to supplement them with practical examples from your experience.

Keywords

• Supervised Learning
• Unsupervised Learning
• Reinforcement Learning
• Overfitting
• Confusion Matrix
• Deep Learning
• Naive Bayes Classifier
• Reinforcement Learning
• Precision and Recall
• Decision Tree Pruning
• Logistic Regression
• K-Nearest Neighbor (KNN)

FAQ

Q: What are the types of machine learning?

A: The main types are supervised learning, unsupervised learning, and reinforcement learning.

Q: What is overfitting and how can it be avoided?

A: Overfitting occurs when a model performs well on training data but poorly on new data. It can be avoided using techniques like regularization.

Q: How do you handle missing data?

A: Missing data can be handled by removing records with missing values, or by filling missing values using methods like mean substitution.

Q: What is a confusion matrix?

A: A confusion matrix is used to evaluate the performance of a classification model, showing the counts of true positive, false positive, false negative, and true negative predictions.

Q: What is the difference between supervised and unsupervised learning?

A: Supervised learning uses labeled data to train models, while unsupervised learning uses unlabeled data.

Q: What is logistic regression used for?

A: Logistic regression is used for binary classification, predicting probabilities for class membership.

Q: How does K-Nearest Neighbor (KNN) work?

A: KNN classifies a data point based on the majority class of its 'k' nearest neighbors.