Quick-Notes-for-ML-DS

It contains interview preparation notes provided by iNeuron, article links.

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
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

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. ML Design Link