/Comprehensive-Data-Science-Notes

Data Science Notes collected from Various Sources

COMPREHENSIVE DATA SCIENCE NOTES

HAPPY LEARNING ⊂◉‿◉つ

It contains Data Science Notes collaborated from various Data Science Experts. Happy Learning !!

💡 Important Concepts:

  1. What is the difference between filter, wrapper, and embedded methods for feature selection? Answer
  2. 120 Questions. Answer
  3. Probability vs. Likelihood. Answer My Fav.: StatQuest
  4. Generative and discriminative. Answer
  5. ML concepts and code. Answer
  6. EM - Expectation-Maximization. Answer
  7. Random Forest. Answer
  8. Regression - Type of change. Answer
  9. Pearson vs Spearman vs Kendall: Stackexchange
  10. Gain and Lift Charts. listendata
  11. Statistical Hypothesis tests in Python. Jason
  12. Machine learning system design. Link
  13. A/B Testing. Link, Link
  14. Product Questions. Quora
  15. Random Forest to Layman. Quora
  16. ANOVA, ANCOVA etc. Link
  17. ML System Design Template Link

Useful blogs to refer:

  1. Martin Henze (Heads or Tails). Blog
  2. Python Snippets. Link
  3. PandasVault. Link
  4. Python Engineer. Twitter
  5. Paired vs Unpaired data: link
  6. Data informed product building: Link
  7. Metric: Link, Link,SQL
  8. Into to Linear Algebra: Link
  9. IMS data sources: Link
  10. Predictive model performance check: ListenData
  11. Case Study: Link
  12. Collection of cases: Link, GAME
  13. Gradient Boosting: Link
  14. Federated learning: Link, Link2
  15. MLOps: Link
  16. Mixed Effect Models: Link, Link1
  17. ML System feature store: Link
  18. Data Science Cheat Sheet: Link
  19. Things can go wrong: Link
  20. Transformers from scratch Link
  21. Dive into Deep Learning Link
  22. DL Interview Link
  23. DL Rules of Thumb Link
  24. ML Forecasting Link
  25. MLOps without much Ops Link
  26. Rules of Machine Learning by Google Link
  27. Product Management for AI Link

ML System Design:

  1. Framework Link
  2. Product minded ML design. Link
  3. ML Design Link
  4. MLE Book Link
  5. ML System design Link
  6. Full stack deep learning Link
  7. Production Machine Learning Problems Link
  8. ML System Design Resources Link
  9. Metric Question Link
  10. Product Matrics Link
  11. ML Stack Template Link
  12. Patrick Halina - ML Design Link
  13. ML Interview Link
  14. ML Cheat Sheet Link
  15. ML Project Timelines Link

Useful LinkedIn Posts:

Understand the business context first, don't get over-excited about the tech, and jump into coding too early.

When someone asks you for a model, always ask:

👉 why do you need it?

👉 what is your current solution (e.g. what is the baseline to beat)?

👉 who is going to use the predictions and how?

👉 what is the financial impact of the model’s downtime or mistakes?

👉 which metrics do we care about to measure what?

Once you have your answers, back them up with a solid exploratory data analysis, and, when done, loop in the biz team again.

This is a critical moment as your results will translate into 3 potential outcomes:

💡 “Really? This contradicts what I thought. Well, in this case, the ML model doesn’t make much sense anymore”. You are off the hook without a single line of code 🔴

💡 “Ah, interesting. I guess we’ll have to change requirements/scope then.” Course-correct before moving forward 🟠

💡 “This is what I expected. Let’s go ahead”. Green light 🟢