Certificate in ML Design Best Practices
-- ViewingNowThe Certificate in ML Design Best Practices is a comprehensive course that empowers learners with essential skills for designing successful machine learning (ML) models. This course is critical for professionals seeking to advance their careers in ML engineering, data science, and AI research.
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โข Introduction to Machine Learning Design: Understanding the basics of machine learning design, its applications, and best practices.
โข Data Preparation and Preprocessing: Techniques for data cleaning, transformation, and normalization for optimal machine learning performance.
โข Feature Engineering: Strategies for creating meaningful features to improve model accuracy, including dimensionality reduction.
โข Model Selection: Selecting the right algorithm for the problem at hand, considering factors such as model interpretability, computational cost, and performance.
โข Model Evaluation and Validation: Techniques for assessing model performance, avoiding overfitting, and selecting the best model.
โข Hyperparameter Tuning: Methods for optimizing model performance by adjusting hyperparameters, including grid search and random search.
โข Bias-Variance Tradeoff: Striking the right balance between model complexity and generalization, to avoid underfitting or overfitting.
โข Ethical Considerations in ML Design: Understanding the ethical implications of machine learning, including issues of fairness, transparency, and privacy.
โข Deployment and Monitoring: Best practices for deploying and monitoring machine learning models in production, considering model explainability and versioning.
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