Enhance ML Model Accuracy with Hyperparameter Tuning: Grid Search vs. Random Search [Lecture 16] скачать в хорошем качестве

Enhance ML Model Accuracy with Hyperparameter Tuning: Grid Search vs. Random Search [Lecture 16]

Dive deep into the essentials of machine learning model optimization in this comprehensive video! We explore key concepts like training-testing accuracy gaps, parameter tuning, and the crucial role of validation splits. Follow along as we provide both theoretical insights and hands-on demonstrations. Perfect for beginners and enthusiasts aiming to sharpen their skills. 📌 Timestamps 00:00 - Possible Reasons Behind High Training, Low Testing Accuracy 02:52 - Difference Between Parameter and Hyper-parameter 08:10 - Need of Train-Validation-Test Split 10:12 - Hands-on: Train-Validation-Test Split 19:24 - Hyper-parameter Tuning: Comparison of Grid and Random Search 27:11 - Hands-on: Hyper-parameter Tuning Using Grid and Random Search 🎓 Meet Your Instructor Pritam Kudale, an experienced AI educator with 10+ years of expertise, is dedicated to equipping educators with modern AI/ML teaching strategies and tools. 🔗 Connect with Pritam Kudale: LinkedIn Profile   / pritam-kudale-90793236   🔗 Relevant Links: Check out more resources in the description below for datasets, code snippets, and additional reading material : https://github.com/pritkudale/ML-for-... Follow our newsletter at: https://vizuara.ai/email-newsletter/