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Introduction
What is Machine Learning?
What Types of Machine Learning Exist?
Classification and Regression
Parametric and Non-Parametric Models
🔒 Extrapolation and Interpolation
🔒 How do you stay up-to-date with ML?
What is Ensemble Learning?
Generative and Discriminative Models
Data Processing
🔒 How do you approach feature selection and engineering?
🔒 How to Handle Imbalanced Datasets?
🔒 Techniques for Data Normalization
How to Handle Missing Data?
Neural Networks
Shallow and Deep Neural Networks
What is an Activation Function?
What is Gradient Descent?
What is Backpropagation?
What is Transfer Learning?
🔒 Convolutional Neural Networks
Training
🔒 What are some common issues during training models?
What is Overfitting?
🔒 Bias-variance Tradeoff
🔒 Regularization Techniques
🔒 Cross-validation
Evaluation
🔒 How do you determine the performance of your model?

Page Contents

Clarify the Question
Plan Your Approach
Break the problem into smaller parts
Outline your solution
Discuss potential issues
Use Personal Experience
Conclusion

Machine Learning

Interview Questions

Introduction

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