Thread by @AtharvaIngle7

How to learn a Machine Learning algorithm?
Everything you need to consider while approaching to learn a #MachineLearning algorithm 👇

A thread 🧵

1. Get the intuition behind the algorithm (i.e its core ideas and why the algorithm is there in the first place).
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2. Get the mathematical intuition behind the algorithm (understand the math working under the hood).
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3. For what the algorithm is used (regression/classification/both) and how it is modified to fit different scenarios.
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4. How the algorithm works with numerical and categorical data?
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5. How the algorithm works with different types of data (tabular, text, etc).
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6. Impact of outliers and missing data on the algorithm.
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7. Is feature scaling required or not and why?
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8. Impact of the algorithm on imbalanced data and how to fix that?
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9. Bias-Variance Trade-off for the algorithm.
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10. Assumptions of the algorithm.
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11. Where it can be used and where not? (this point is kind-of combination of the above-mentioned points)
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12. Understand the Time-Space complexity of it.
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13. Implement it with scikit-learn and read the docs (pretty well-explained docs).

14. Get a brief idea of what each parameter in the scikit-learn function does (again, read it from the docs).
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15. Extensions of the algorithm (if any).
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16. Advantages and Disadvantages of it.

17. Interpretability of the algorithm.
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18. Implement it from scratch (optional, but highly recommended).