This episode covers the ten essential machine learning questions. Disclaimer: Baseline answers have been provided in the episode for guidance. For complete accuracy, please refer to textbooks or to courses by Andrew Ng on Coursera. 

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Resources:
1. Machine Learning Course by Andrew Ng: https://www.coursera.org/learn/machine-learning
2. Deep Learning Course by Andrew Ng: https://www.coursera.org/specializations/deep-learning

Questions:
1. What is underfitting and overfitting? How to avoid it?
2. What is the difference between batch, SGD and mini-batch gradient descents? When will you use each?
3. How to choose a machine learning model?
4. How to improve the latency of a machine learning model in production?
5. If your training and cross validation accuracies are high, but testing accuracy is less - how would you debug this?
6. Name 3 hyper-parameters. Why can’t we train them as hyper-parameters, why should only humans set them?
7. Which metric should be used to evaluate a classifier? How do you connect it to business value?
8. What prevents someone to select deep learning model for everything?
9. Say you have to classify a lot of data, but you don’t have labelled training examples. How would you begin to solve the problem? How many training data points are needed?
10. Say you have a perfectly working machine learning model. How do you deploy this in production? How do you check if users will actually like it?

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