Taylor Scott Amarel

Experienced developer and technologist with over a decade of expertise in diverse technical roles. Skilled in data engineering, analytics, automation, data integration, and machine learning to drive innovative solutions.

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Practical Model Selection and Hyperparameter Tuning for Machine Learning: A Hands-On Approach

Introduction: The Importance of Model Selection and Hyperparameter Tuning In the realm of machine learning, achieving optimal model performance is paramount. This hinges on two critical processes: model selection and hyperparameter tuning. Selecting the right machine learning model, analogous to choosing the right tool for a job, sets the foundation for success. A naive Bayes

Practical Guide to L1 and L2 Regularization for Machine Learning Models

Introduction to Regularization In the realm of machine learning, the pursuit of a highly performant model often leads to a critical pitfall known as overfitting. Overfitting occurs when a model learns the training data too well, capturing noise and intricacies that are specific to that dataset but not representative of the underlying data distribution. Consequently,