HomeCoursesMastering Hyperparameter Optimization for Machine Learning
Intermediate
5h
Mastering Hyperparameter Optimization for Machine Learning
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Delve into hyperparameter optimization for machine learning models, exploring techniques like grid search, SMBO, TPE, and genetic algorithms using real-world datasets to enhance model performance.
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Course Overview
Machine learning models excel in classification, regression, anomaly detection, language translation, and more. Optimizing hyperparameters can enhance the performance of most machine learning models.
This course will equip you with the skills to optimize hyperparameters for various machine learning models. You’ll begin with the introduction of hyperparameters and understand the need for optimizing them. Using a loan approval dataset for binary classification, you’ll explore both random and grid search met...Show More
Machine learning models excel in classification, regression, anomaly detection, language translation, and more. Optimizing hyper...Show More
WHAT YOU'LL LEARN
Familiarity with hyperparameter optimization methods, including random search, grid search, and sequential model-based optimization
Hands-on experience configuring, implementing, and evaluating hyperparameter optimization techniques using Python
Understanding the advantages and disadvantages of the various hyperparameter optimization methods
Working knowledge of Python libraries such as scikit-learn, TPOT, scikit-optimize, and Optuna for hyperparameter optimization
Familiarity with hyperparameter optimization methods, including random search, grid search, and sequential model-based optimization
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TAKEAWAY SKILLS
Python
Data Science
Machine Learning
Course Content
37 Lessons1 Project5 Quizzes1 Assessment
1.
Introduction
4 Lessons
Get familiar with hyperparameters, their optimization, and the dataset for machine learning models.
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