Course
Hyperparameter Tuning in Python
СреднийУровень мастерства
Обновлено 04.2023Начать Курс Бесплатно
В комплекте сПремиум or Команды
PythonMachine Learning4 ч13 videos44 Exercises3,400 XP24,167Свидетельство о достижениях
Пользуется популярностью среди обучающихся в тысячах компаний.
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Hyperparameters vs. parameters
Gain practical experience using various methodologies for automated hyperparameter tuning in Python with Scikit-Learn.Learn the difference between hyperparameters and parameters and best practices for setting and analyzing hyperparameter values. This foundation will prepare you to understand the significance of hyperparameters in machine learning models.
Grid search
Master several hyperparameter tuning techniques, starting with Grid Search. Using credit card default data, you will practice conducting Grid Search to exhaustively search for the best hyperparameter combinations and interpret the results.You will be introduced to Random Search, and learn about its advantages over Grid Search, such as efficiency in large parameter spaces.
Informed search
In the final part of the course, you will explore advanced optimization methods, such as Bayesian and Genetic algorithms.These informed search techniques are demonstrated through practical examples, allowing you to compare and contrast them with uninformed search methods. By the end, you will have a comprehensive understanding of how to optimize hyperparameters effectively to improve model performance.
Предварительные требования
Supervised Learning with scikit-learn1
Hyperparameters and Parameters
In this introductory chapter you will learn the difference between hyperparameters and parameters. You will practice extracting and analyzing parameters, setting hyperparameter values for several popular machine learning algorithms. Along the way you will learn some best practice tips & tricks for choosing which hyperparameters to tune and what values to set & build learning curves to analyze your hyperparameter choices.
2
Grid search
This chapter introduces you to a popular automated hyperparameter tuning methodology called Grid Search. You will learn what it is, how it works and practice undertaking a Grid Search using Scikit Learn. You will then learn how to analyze the output of a Grid Search & gain practical experience doing this.
3
Random Search
In this chapter you will be introduced to another popular automated hyperparameter tuning methodology called Random Search. You will learn what it is, how it works and importantly how it differs from grid search. You will learn some advantages and disadvantages of this method and when to choose this method compared to Grid Search. You will practice undertaking a Random Search with Scikit Learn as well as visualizing & interpreting the output.
4
Informed Search
In this final chapter you will be given a taste of more advanced hyperparameter tuning methodologies known as ''informed search''. This includes a methodology known as Coarse To Fine as well as Bayesian & Genetic hyperparameter tuning algorithms. You will learn how informed search differs from uninformed search and gain practical skills with each of the mentioned methodologies, comparing and contrasting them as you go.
Hyperparameter Tuning in Python
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