AI/ML Notes

Welcome to My AI/ML Book

This is my newly started pet project and an evolving book.

content will appear soon. :)

Things to include

Data preparation

1. Handling large dataset
2. Handling high dimensionality models
3. Handling missing data
4. Feature engineering
5. Handling class imbalances (smot library)

Various model and its evaluation metrices

1. Various ML models along with best practices
2. clustering algorithms (including WCSS plot)
3. Deep networks (drop off/Early stop/branch pruning/embedding)
4. Evaluation metrics

high level informations…

1. Model interpretibility (Shapley models,Pydotplus tree visualisers ,latest paper of EPFL etc)
2. Model monitoring (Drift analysis etc)
3. Model Training vs retraining
4. Bias variance tradeoffs
5. SciML & PINN
6. Model deployment logics
7. Data pipeline managements