Machine Learning Engineer Interview Resources

Here are some links to machine learning theoretical concepts and practical advice typically asked in an ML interview. Please let me know any comments or advice that you have.

  1. General foundation
    1. Stanford’s Machine Learning course taught by Prof. Andrew Ng
    2. QUT’s Data and Web Analytics course taught by Prof. Richi Nayak
    3. Practical advices from Machine Learning Mastery (
  2.  Classification
    1. KNN (K nearest neighbor) algorithm
    2. Decision tree/random forest
    3. Confusion matrix, precision, recall, F1
    4. Neural network
  3. Ensemble method
  4. Recommendation system
    1. Matrix factorisation for collaborative filtering
    2. Learn to rank
    3. NDCG
  5. Text mining
    1. TF-IDF representation
    2. Naïve Bayes
    3. Conditional random fields
    4. Latent Dirichlet Allocation
  6. Image processing/computer vision
    1. CNN – Convolutional Neural Network
    2. Transfer learning
  7. Some math and statistical theorems
    1. Bayes theorem
  8. Practical advices and other topics
    1. Feature engineering
    2. Difference between L1 and L2
    3. How to handle skewed data set
    4. GPU vs CPU
    5. Online learning
    6. Batch, stochastic and mini-batch gradient descent
    7. Vanishing gradient problem
    8. Kernel trick
    9. K-fold cross validation
    10. Semi-supervised learning
    11. Real life examples
  9. Interview Questions

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