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Exploring how Retrieval Augmented Generation helps large language models access external knowledge, reduce hallucinations, and deliver more reliable responses by knowing when to retrieve rather than generate.

Explore the difference between AI Agents and Agentic AI with real-world examples.

Discovering the Power of Dimensionality Reduction - Unveiling patterns and visualizing data with PCA and t-SNE.

Hyperparameter tuning in machine learning, including methods such as grid search, random search, and Bayesian optimization and providing code snippets to demonstrate how to use these techniques in practice.

Model interpretability in machine learning, including the use of LIME and SHAP techniques