Alright, so you’re gearing up for a machine learning coding interview and maybe feeling a bit nervous about what kind of questions they might throw your way. Don’t worry—you’re not alone. ML interviews can feel a little intense, especially when you’re asked to code solutions on the spot. But with the right set of questions to practice, you’ll do just fine!
Machine Learning Coding Interview Questions
Here’s a handy list of some of the most commonly asked machine learning coding interview questions, along with short and simple explanations to help you feel more confident walking into that room (or Zoom call).
1. Implement Linear Regression From Scratch
You’ll probably be asked to code linear regression without using libraries like scikit-learn.
You should know:
- How to calculate the slope (m) and intercept (b)
- How to minimize error using the least squares method
- Optionally, how to implement gradient descent
2. Write Code for Logistic Regression Without Using Any ML Library
This is very common too. Logistic regression is used for classification, so you need to:
- Implement the sigmoid function
- Use binary cross-entropy loss
- Train using gradient descent
Bonus points if you handle multiple iterations and show convergence.
3. Build a Simple Decision Tree
They might ask you to code a decision tree using:
- Information gain or Gini impurity
- Recursive splitting
- Handling of stopping conditions (max depth, min samples, etc.)
You won’t need a perfect sklearn-level tree—just a basic working version.
4. K-Nearest Neighbors (KNN) Implementation
This one’s pretty straightforward. You’ll probably be asked to:
- Calculate Euclidean distance
- Sort distances
- Pick the top-k and do majority voting
It’s mostly about loops and distance math.
5. Code a K-Means Clustering Algorithm
Clustering is unsupervised, so you won’t have labels. You’ll need to:
- Randomly pick initial centroids
- Assign points to nearest centroid
- Update centroids by calculating the mean
- Repeat until convergence
Good to show you understand iterative algorithms.
6. Implement Naive Bayes Classifier
This involves:
- Calculating prior probabilities of each class
- Calculating likelihoods of features per class
- Using Bayes’ theorem to predict classes
If it’s text-based (like spam detection), you might use bag-of-words.
7. Write a Function to Calculate Precision, Recall, and F1-Score
These metrics are often used to evaluate classification models.
You should know how to:
- Count true positives, false positives, and false negatives
- Use formulas to calculate precision, recall, and F1-score
Also helpful if you explain when to use each metric (imbalanced datasets, etc.).
8. Gradient Descent Implementation
They might ask you to write gradient descent for a simple cost function.
You should understand:
- Derivatives
- Learning rate
- Updating weights based on gradients
Even better if you show how it converges with a loop.
9. Build a Simple Neural Network
This might sound scary, but sometimes they just want a 1-hidden-layer NN. You need to:
- Initialize weights and biases
- Forward pass (with activation functions)
- Backpropagation
- Update weights
Keep it minimal but working. Bonus if you do classification with softmax.
10. Perform Feature Scaling / Normalization
They could ask for a standard scaler or min-max normalization.
Example:
X_scaled = (X - X.mean()) / X.std()
Understand when and why you scale features—especially for algorithms like SVM or KNN.
11. Write PCA From Scratch
Principal Component Analysis sounds intense, but focus on the core:
- Center the data (subtract mean)
- Calculate the covariance matrix
- Find eigenvectors/eigenvalues
- Select top k eigenvectors
- Project the data
No need to go too deep—just the process matters.
12. Create a Simple Spam Classifier Using Python
This could be a practical case-based coding challenge. You’d need to:
- Clean and tokenize the text
- Use term frequencies or TF-IDF
- Train a simple Naive Bayes model
Explain your preprocessing steps clearly.
13. Data Cleaning Code
They might ask for:
- Removing nulls
- Imputing missing values
- Handling duplicates
- Encoding categorical variables
Just show your grasp on data preprocessing—it’s super important.
14. Train-Test Split Without Using Scikit-learn
Very basic but often asked. You could randomly shuffle the dataset and split it manually using slicing.
15. Create a Function to Calculate RMSE and MAE
You should know how to code:
- Mean Absolute Error (MAE)
- Root Mean Square Error (RMSE)
They’re used to evaluate regression models.
16. Cross-Validation Implementation (Basic Version)
If not using sklearn, show how you can:
- Split data into k-folds
- Train on k-1 and test on 1
- Repeat and average the results
Even a 3-fold example is enough.
17. Implement a Confusion Matrix
This helps evaluate classifiers.
You’ll need to count:
- True Positives (TP)
- False Positives (FP)
- True Negatives (TN)
- False Negatives (FN)
Then format it into a matrix and optionally plot it.
18. Hyperparameter Tuning Using Grid Search (Basic)
Even if not using GridSearchCV, show how you would:
- Define a few values for hyperparameters
- Train model for each combo
- Pick the one with best validation score
19. Simple Recommender System (Collaborative Filtering)
They might give you a user-item matrix and ask you to:
- Fill in missing values
- Recommend items based on similarity (cosine similarity or Pearson)
A basic user-user collaborative filter works well here.
20. Write a Custom Loss Function
You could be asked to write:
- Mean Squared Error (MSE)
- Cross-Entropy Loss
Make sure you understand how loss functions guide model training.
Quick Tips Before You Go:
- Practice Python + NumPy – A lot of interviews are Python-based.
- Brush up on math – Matrix operations, probability, and basic calculus matter.
- Write clean code – Always include comments and break your logic into functions.
- Explain as you code – Most interviewers love when you walk through your thought process.
Want more help with interview prep, cheat sheets, or mock questions? Just let me know—I’ve got loads of ideas to help you out!
