Global Certificate in ML Model Performance Tuning

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The Global Certificate in ML Model Performance Tuning course is a comprehensive program that focuses on the crucial aspect of machine learning model development - performance tuning. This course is designed to equip learners with essential skills to improve the accuracy and efficiency of machine learning models, thereby making them more valuable in the industry.

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In today's data-driven world, there is an increasing demand for professionals who can build and optimize machine learning models. This course is an excellent opportunity for learners to enhance their skillset and stay ahead in the competition. The course covers various techniques, tools, and best practices for model performance tuning, making it a valuable asset for anyone looking to advance their career in machine learning. By the end of this course, learners will be proficient in optimizing machine learning models for better performance, reducing computational cost, and improving model accuracy. They will have a deep understanding of various hyperparameter tuning techniques, model selection, and evaluation metrics. This course will provide learners with a solid foundation in machine learning model performance tuning, enabling them to take on more complex projects and advance their careers.

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โ€ข Introduction to Machine Learning Model Performance Tuning
โ€ข Understanding Evaluation Metrics for ML Models
โ€ข Hyperparameter Tuning Techniques in Machine Learning
โ€ข Grid Search and Random Search for ML Model Tuning
โ€ข Cross-Validation Techniques for ML Model Evaluation
โ€ข Regularization Techniques for ML Model Performance
โ€ข Ensemble Methods for Improving ML Model Performance
โ€ข Feature Selection and Engineering for ML Model Optimization
โ€ข Using Cloud-Based Platforms for ML Model Tuning
โ€ข Best Practices and Strategies for ML Model Performance Tuning

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GLOBAL CERTIFICATE IN ML MODEL PERFORMANCE TUNING
ๆŽˆไบˆ็ป™
ๅญฆไน ่€…ๅง“ๅ
ๅทฒๅฎŒๆˆ่ฏพ็จ‹็š„ไบบ
London School of International Business (LSIB)
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05 May 2025
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