Practical guide for software engineers diving into machine learning, focusing on understanding F1-Score, why it matters for imbalanced datasets like Titanic survival prediction, and how to effectively use it in model evaluation.
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From Software Dev to ML: Mastering GridSearchCV - Automating Hyperparameter Tuning
Practical guide for software engineers diving into machine learning, focusing on understanding and implementing GridSearchCV for automated hyperparameter tuning, with real examples from the Titanic survival prediction dataset.
From Software Dev to ML: Defining the Titanic Survival Prediction Problem
Practical guide for software engineers transitioning into machine learning, focusing on Phase 1 of the ML lifecycle using the classic Titanic survival prediction problem as a case study. It walks through the process of clearly defining the problem, setting measurable success metrics like accuracy, checking data feasibility, and identifying constraints and assumptions.
From Software Dev to ML: My Deep Dive into the Machine Learning Lifecycle
A practical guide to the machine learning (ML) lifecycle, written from the perspective of a software engineer. It breaks down the process into seven key phases, from problem definition and data collection to model deployment and ongoing monitoring.
Ensuring Object Validity
Best practices for ensuring object validity in software development, focusing on how and where to validate objects based on the architecture and framework being used.