Machine Learning Interview Questions Table
| ID▼ | Title▼ | Content | Tags | Categories |
|---|---|---|---|---|
| 1088 | ML0066 Model Capacity | Without activation functions, how … | NN | Hard |
| 943 | ML0065 Random Forest III | How to choose the number of featur… | Tree | Easy |
| 938 | ML0064 Random Forest II | Please explain the benefits and dr… | Tree | Medium |
| 921 | ML0063 Random Forest | How does the random forest algorit… | Tree | Easy |
| 913 | ML0062 Decision Tree | Please explain how a decision tree… | Tree | Easy |
| 907 | ML0061 KNN and K-means | What are the key differences betwe… | Kmeans KNN | Easy |
| 896 | ML0060 K Selection in K-Means | How to select K in K-Means? | Kmeans | Medium |
| 890 | ML0059 K-means II | K-Means is widely used for cluster… | Kmeans | Easy |
| 881 | ML0058 K-means++ | Please explain how K-means++ works… | Kmeans | Medium |
| 876 | ML0057 K-means | Please explain how K-means works. … | Kmeans | Easy |
| 868 | ML0056 K Selection in KNN | In the context of designing a K-Ne… | KNN Validation | Medium |
| 863 | ML0055 KNN Regression | Please explain how KNN Regression … | KNN | Easy |
| 850 | ML0054 KNN Classification | Please explain how KNN classificat… | KNN | Easy |
| 844 | ML0053 Hinge Loss for SVM | Explain the Hinge Loss function us… | Loss SVM | Easy |
| 834 | ML0052 Non-Linear SVM | Can you explain the concept of a n… | SVM | Medium |
| 820 | ML0051 Linear SVM | Can you explain the key concepts b… | SVM | Easy |
| 817 | ML0050 Logistic Regression III | Why is Mean Squared Error (L2 Loss… | Basics Loss | Medium |
| 814 | ML0049 Logistic Regression II | Please compare Logistic Regression… | Basics NN | Easy |
| 803 | ML0048 Logistic Regression | Can you explain logistic regressio… | Basics | Easy |
| 755 | ML0047 Parameters | What are the differences between p… | Basics | Easy |
| 750 | ML0046 Forward Propagation | Please explain the process of Forw… | Basics NN | Easy |
| 735 | ML0045 Multi-Layer Perceptron | What is a Multi-Layer Perceptron (… | Basics NN | Easy |
| 727 | ML0044 Perceptron | Describe the Perceptron and its li… | Basics NN | Easy |
| 699 | ML0043 Feature Scaling | Walk me through the rationale behi… | Data | Easy |
| 696 | ML0042 Early Stopping | What is Early Stopping? How is it … | Basics Validation | Easy |
| 688 | ML0041 Concept of NN | Please explain the concept of a Ne… | Basics NN | Easy |
| 668 | ML0040 Bias and Variance | Can you explain the bias-variance … | Basics Fit | Easy |
| 666 | ML0039 Distributed Training | What are the two main distributed … | Basics | Easy |
| 663 | ML0038 Validation and Test | What are the key purposes of using… | Data Validation | Easy |
| 661 | ML0037 Bias in NN | Why is bias used in neural network… | NN | Easy |
| 658 | ML0036 Confusion Matrix | In which scenarios is a Confusion … | Metrics | Easy |
| 655 | ML0035 Model Comparison | How to compare different machine l… | Basics Validation | Easy |
| 633 | ML0034 Backpropagation | What is backpropagation? | Basics NN | Easy |
| 628 | ML0033 All Zeros Init | How does initializing all weights … | Basics NN | Medium |
| 622 | ML0032 Non-Linear Activation | Why use non-linear activation func… | Basics | Easy |
| 438 | ML0031 Linear Regression | What are the advantages and disadv… | Basics | Easy |
| 433 | ML0030 Sigmoid | What are the advantages and disadv… | Basics | Easy |
| 427 | ML0029 Tanh | What are the advantages and disadv… | Basics | Medium |
| 393 | ML0028 Softmax | What is the Softmax activation fun… | Basics | Easy |
| 378 | ML0027 Leaky ReLU | What are the benefits of the Leaky… | Basics | Medium |
| 373 | ML0026 ReLU | What are the benefits and limitati… | Basics | Easy |
| 368 | ML0025 Exploding Gradient | What are the typical reasons for e… | Basics | Medium |
| 361 | ML0024 Vanishing Gradient | What are the typical reasons for v… | Basics | Medium |
| 348 | ML0023 Gradient Descent | What is Gradient Descent in machin… | Basics | Easy |
| 337 | ML0022 Cross Entropy Loss | Explain how Cross Entropy Loss is … | Loss | Easy |
| 315 | ML0021 L1 Loss L2 Loss | What are the key differences betwe… | Loss | Easy |
| 312 | ML0020 Data Split | How to split the dataset? | Basics Data | Easy |
| 309 | ML0019 Imbalanced Data | How to handle imbalanced data in M… | Data | Medium |
| 306 | ML0018 Data Normalization | Why is data normalization used in … | Data Norm | Easy |
| 304 | ML0017 Data Augmentation | What are the common data augmentat… | Basics Data | Easy |
| 301 | ML0016 AUC | What is AUC? | Metrics | Easy |
| 295 | ML0015 ROC Curve | What is the ROC Curve, and how is … | Metrics | Medium |
| 274 | ML0014 Confusion Matrix | What is the confusion matrix?… | Metrics | Easy |
| 265 | ML0013 Accuracy | What is accuracy? | Metrics | Easy |
| 260 | ML0012 F1 Score | What is F1 Score? | Metrics | Easy |
| 239 | ML0011 Precision and Recall | What are Precision and Recall?… | Metrics | Easy |
| 237 | ML0010 Epoch Selection | What are effective strategies for … | Basics | Easy |
| 235 | ML0009 Batch Size Selection | What are the best strategies for s… | Basics | Easy |
| 233 | ML0008 Learning Rate Selection | What are the best practices for se… | Basics | Easy |
| 230 | ML0007 Dropout | What is dropout in neural network … | Basics NN | Medium |
| 224 | ML0006 Cross-Validation | What are the common cross-validati… | Validation | Medium |
| 215 | ML0005 Discriminative and Generative | What are the differences between d… | GenAI | Medium |
| 213 | ML0004 Underfitting | Which of the following description… | Fit | Easy |
| 208 | ML0003 Overfitting | What is overfitting and how to avo… | Fit | Easy |
| 205 | ML0002 Machine Learning Type | What is the difference between sup… | Basics | Easy |
| 172 | ML0001 Loss Curve Plot | The following training loss curves… | Basics Loss | Easy |