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This course explores the essential process of optimizing model performance by adjusting hyperparameters. Learners will learn advanced techniques for fine-tuning machine learning models to achieve optimal results. Topics covered include grid search, random search, Bayesian optimization, and other methods. Through practical exercises and real-world examples, individuals will gain hands-on experience in selecting and tuning hyperparameters effectively. By the end of the course, learners will be equipped with the knowledge and skills necessary to optimize model performance across various machine learning tasks.
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Free
100% positive reviews
2 students
8 lessons
English
0 quiz
Self Assessments
Available on the app
Unlimited access forever
Skill level All levels
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