Machine Learning

Articles

ML algos

Kaggle

• Repo
• Kaggle Tricks

Steps:

1. EDA
2. Feature engineering
3. Build ML model
4. Model validation

random_state parameter

Many machine learning models allow some randomness in model training. Specifying a number for random_state ensures you get the same results in each run. You use any number, and model quality won't depend meaningfully on exactly what value you choose.

Underfitting vs overfitting

Control the balance between these two using max leaves and max tree depth parameters in decision trees.

MLOps

Weights and Biases

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Children

1. Casual Impact
2. Code ML from scratch
3. Comprehensive maths behind ML
4. Cross Validation
5. Curse of Large Dimensionality
6. DT RF GB
7. Deep Learning
8. Diff and diff analysis
9. Experiments
10. Explainable AI - SHAP
11. Feature Engineering
12. Google Rules of Machine Learning
13. Imbalanced dataset
14. Kaggle Tricks
15. Kaggle winning solutions
16. Logistic Regression
17. Loss Functions
18. Metrics
19. Outliers
20. Time Series