Developing Machine Learning Models
Building an effective machine learning model involves far more than selecting the latest algorithm. Using a fraud detection project, this video follows the complete development journey from defining the problem, engineering features and establishing a baseline to comparing algorithms, tuning hyperparameters, debugging training and tracking experiments. Discover how classical models compare with neural networks, why state-of-the-art systems may fail in production, and how distributed training and AutoML support modern workflows. Most importantly, learn why the best model is often the simplest one that solves the real problem reliably.
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