Summary
In our study of machine learning, we delved deeply into crucial concepts, obtaining significant insights. Our exploration spanned both supervised and unsupervised learning, equipping us with a diverse set of models.
In this chapter, we harnessed models ranging from linear and logistic regression to tree-based techniques such as random forests and XGBoost. These models have enabled us to capture intricate relationships and accurately estimate class probabilities. Additionally, our foray into clustering methods, including K-means, hierarchical clustering, and DBSCAN, has allowed us to master the art of extracting patterns from unlabeled data. Furthermore, our knowledge has been augmented with vital skills in hyperparameter tuning and model evaluation. We learned how to refine models using tools such as grid search and have come to understand key evaluation metrics, such as accuracy and precision.
As we gear up for data science interviews, this knowledge stands as a testament...
