Preparing for a #MachineLearning #DataScience interview?
One retweet - one theoretical interview question in the thread 👇
Feel free to give your answers
Let's start!
#100DaysOfCode #100DaysOfMLCode
One retweet - one theoretical interview question in the thread 👇
Feel free to give your answers
Let's start!
#100DaysOfCode #100DaysOfMLCode
Interview questions are typically based on what the company needs and/or projects you have worked with previously.
So if you didn’t work with time series - it’s unlikely you’ll get many questions about it. Same with computer vision, NLP or recommender systems.
So if you didn’t work with time series - it’s unlikely you’ll get many questions about it. Same with computer vision, NLP or recommender systems.
Important: don’t feel discouraged if you don’t know the answers to some of the interview questions, this is absolutely fine.
Difficulty:
- 👶 easy
- 👩🎓 medium
- 🛠️ expert
(Subjective - based on my opinion)
- 👶 easy
- 👩🎓 medium
- 🛠️ expert
(Subjective - based on my opinion)
What is supervised machine learning? 👶
What is regression? Which models can you use to solve a regression problem? 👶
What is linear regression? When do we use it? 👶
What’s the normal distribution? Why do we care about it? 👶
How do we check if a variable follows the normal distribution? 👩🎓
What if we want to build a model for predicting prices? Are prices distributed normally? Do we need to do any pre-processing for prices? 👩🎓
What are the methods for solving linear regression do you know? 👩🎓
What is gradient descent? How does it work? 👩🎓
What is the normal equation? 👩🎓
What is SGD - stochastic gradient descent? What’s the difference with the usual gradient descent? 👩🎓
Which metrics for evaluating regression models do you know? 👶
What are MSE and RMSE? 👶
What is overfitting? 👶
How to do you validate your models? 👶
Why do we need to split our data into three parts: train, validation, and test? 👶
Can you explain how cross-validation works? 👶
What is K-fold cross-validation? 👶
How do we choose K in K-fold cross-validation? What’s your favourite K? 👶
What happens to our linear regression model if we have three columns in our data: x, y, z - and z is a sum of x and y? 👩🎓
What happens to our linear regression model if the column z in the data is a sum of columns x and y and some random noise? 👩🎓
What is regularization? Why do we need it? 👶
Which regularization techniques do you know? 👩🎓
What is classification? Which models would you use to solve a classification problem? 👶
What is logistic regression? When do we need to use it? 👶
Is logistic regression a linear model? Why? 👶
What is sigmoid? What does it do? 👶
How do we evaluate classification models? 👶
What is accuracy? 👶
Is accuracy always a good metric? 👶
What is the confusion table? What are the cells in this table? 👶
What is precision, recall, and F1-score? 👶
Precision-recall trade-off 👩🎓
What is the ROC curve? When to use it? 👩🎓
What is AUC (AU ROC)? When to use it? 👩🎓
How to interpret the AU ROC score? 👩🎓
What is the PR (precision-recall) curve? 👩🎓
What is the area under the PR curve? Is it a useful metric? 👩🎓
In which cases AU PR is better than AU ROC? 👩🎓
What do we do with categorical variables? 👩🎓
Why do we need one-hot encoding? 👩🎓
What kind of regularization techniques are applicable to linear models? 👩🎓
How does L2 regularization look like in a linear model? 👩🎓
How do we select the right regularization parameters? 👶
What’s the effect of L2 regularization on the weights of a linear model? 👩🎓
How L1 regularization looks like in a linear model? 👩🎓
What’s the difference between L2 and L1 regularization? 👩🎓
Can we have both L1 and L2 regularization components in a linear model? 👩🎓
What’s the interpretation of the bias term in linear models? 👩🎓
How do we interpret weights in linear models? 👩🎓
If a weight for one variable is higher than for another - can we say that this variable is more important? 👩🎓
When do we need to perform feature normalization for linear models? When it’s okay not to do it? 👩🎓
What is feature selection? Why do we need it? 👶
Is feature selection important for linear models? 👩🎓
Which feature selection techniques do you know? 👩🎓
Can we use L1 regularization for feature selection? 👩🎓
Can we use L2 regularization for feature selection? 👩🎓
What are the decision trees? 👶
How do we train decision trees? 👩🎓
What are the main parameters of the decision tree model? 👶
How do we handle categorical variables in decision trees? 👩🎓
What are the benefits of a single decision tree compared to more complex models? 👩🎓
How can we know which features are more important for the decision tree model? 👩🎓
What is random forest? 👶
Why do we need randomization in random forest? 👩🎓
What are the main parameters of the random forest model? 👩🎓
How do we select the depth of the trees in random forest? 👩🎓
How do we know how many trees we need in random forest? 👩🎓
Is it easy to parallelize training of random forest? How can we do it? 👩🎓
What are the potential problems with many large trees? 👩🎓
What if instead of finding the best split, we randomly select a few splits and just select the best from them. Will it work? 🛠️
What happens to random forest when we have correlated features in our data? 👩🎓
What is gradient boosting trees? 👩🎓
What’s the difference between random forest and gradient boosting? 👩🎓
Is it possible to parallelize training of a gradient boosting model? How to do it? 👩🎓
Feature importance in gradient boosting trees - what are possible options? 👩🎓
Are there any differences between continuous and discrete variables when it comes to feature importance of gradient boosting models? 🛠️
What are the main parameters in the gradient boosting model? 👩🎓
How do you approach tuning parameters in XGBoost or LightGBM? 🛠️
How do you select the number of trees in the gradient boosting model? 👩🎓
Which parameter tuning strategies (in general) do you know? 👩🎓
What’s the difference between grid search parameter tuning strategy and random search? When to use one or another? 👩🎓
What kind of problems neural nets can solve? 👶
How does a usual fully-connected neural network work? 👩🎓
Why do we need activation functions? 👶
What are the problems with sigmoid as an activation function? 👩🎓
What is ReLU? How is it better than sigmoid or tanh? 👩🎓
How we can initialize the weights of a neural network? 👩🎓
What if we set all the weights of a neural network to 0? 👩🎓
What regularization techniques for neural nets do you know? 👩🎓
What is dropout? Why is it useful? How does it work? 👩🎓
What is backpropagation? How does it work? Why do we need it? 👩🎓
Which optimization techniques for training neural nets do you know? 👩🎓
How do we use SGD (stochastic gradient descent) for training a neural net? 👩🎓
What’s the learning rate? 👶
What happens when the learning rate is too large? Too small?👩🎓
How to choose the learning rate? 👩🎓
What is Adam? What’s the main difference between Adam and SGD? 👩🎓
When would you use Adam and when SGD? 👩🎓
Do we want to have a constant learning rate or we better change it throughout training? 👩🎓
How do we decide when to stop training a neural net? 👶
What is model checkpointing? 👩🎓
Can you tell us how you approach the model training process? 👩🎓
How we can use neural nets for computer vision? 👩🎓
What is a convolution? What’s a convolutional layer? 👩🎓
Why do we actually need convolutions? Can’t we use a fully-connected layer for that? 👩🎓
What’s pooling in CNN? Why do we need it? 👩🎓
How max pooling works? Are there other pooling techniques? 👩🎓
Are CNNs resistant to rotations? What happens to the predictions of a CNN if an image is rotated? 🛠️
What are augmentations? Why do we need them? 👶
What kind of augmentations do you know? 👶
How to choose which augmentations to use? 👩🎓
What kind of CNN architectures for classification do you know? 🛠️
What is transfer learning? How does it work? 👩🎓
What is object detection? Do you know any architectures for that? 🛠️
What is object segmentation? Do you know any architectures for that? 🛠️
How can we use machine learning for text classification? 👩🎓
What is bag of words? How we can use it for text classification? 👩🎓
What are the advantages and disadvantages of bag of words? 👩🎓
What are N-grams? How can we use them? 👩🎓
How large should be N for our bag of words when using N-grams? 👩🎓
What is TF-IDF? How it’s useful for text classification? 👩🎓
Which model would you choose for text classification with bag of words features? 👩🎓
Would you prefer gradient boosting trees model or logistic regression when doing text classification with bag of words? 👩🎓
What are word embeddings? Why are they useful? Do you know Word2Vec? 👩🎓
Do you know any other ways to get word embeddings? 🛠️
If you have a sentence with multiple words, you may need to combine multiple word embeddings into one. How would you do it? 👩🎓
Would you prefer gradient boosting trees model or logistic regression when doing text classification with embeddings? 👩🎓
How can you use neural nets for text classification? 🛠️
How can we use CNN for text classification? 🛠️
What is unsupervised learning? 👶
What is clustering? When do we need it? 👶
Do you know how K-means works? 👩🎓
How to select K for K-means? 👩🎓
What are the other clustering algorithms do you know? 👩🎓
Do you know how DBScan works? 👩🎓
When would you choose K-means and when DBScan? 👩🎓
What is the curse of dimensionality? Why do we care about it? 👩🎓
Do you know any dimensionality reduction techniques? 👩🎓
What’s singular value decomposition? How is it typically used for machine learning? 👩🎓
What is the ranking problem? Which models can you use to solve them? 👩🎓
What are good unsupervised baselines for text information retrieval? 👩🎓
How would you evaluate your ranking algorithms? Which offline metrics would you use? 👩🎓
What is precision and recall at k? 👩🎓
What is mean average precision at k? 👩🎓
How can we use machine learning for search? 👩🎓
How can we get training data for our ranking algorithms? 👩🎓
Can we formulate the search problem as a classification problem? How? 👩🎓
How can we use clicks data as the training data for ranking algorithms? 🛠️
Do you know how to use gradient boosting trees for ranking? 🛠️
How do you do an online evaluation of a new ranking algorithm? 👩🎓
What is a recommender system? 👶
What are good baselines when building a recommender system? 👩🎓
What is collaborative filtering? 👩🎓
How we can incorporate implicit feedback (clicks, etc) into our recommender systems? 👩🎓
What is the cold start problem? 👩🎓
Possible approaches to solving the cold start problem? 👩🎓🛠️
