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11: The Ten Essential Machine Learning Questions
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.
If this content is useful, please consider buying me a coffee via the link https://anchor.fm/the-data-life-podcast/support
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?
Please leave a review on Apple Podcasts or wherever you listen to this.
Thanks for listening!
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By Sanket Gupta
5
55 ratings
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.
If this content is useful, please consider buying me a coffee via the link https://anchor.fm/the-data-life-podcast/support
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?
Please leave a review on Apple Podcasts or wherever you listen to this.
Thanks for listening!