The most comprehensive online course on hyperparameter tuning for machine learning. You will learn multiple techniques to select the best hyperparameters and improve the performance of your machine learning models.
If you're disappointed for whatever reason, you'll get a full refund.
Sole is a lead data scientist, instructor and developer of open source software. She created and maintains the Python library for feature engineering Feature-engine, which allows us to impute data, encode categorical variables, transform, create and select features. Sole is also the author of the book "Python Feature engineering Cookbook" by Packt editorial.
Welcome to Hyperparameter Optimization for Machine Learning, the most comprehensive course on hyperparameter tuning available online. In this course, you will learn multiple techniques to select the best hyperparameters and improve the performance of your machine learning models.
Hyperparameters are parameters that are not directly learnt by the machine learning algorithm. They define and control the machine learning model, i.e., how flexible the model is to fit the training data, and they are calibrated to avoid over-fitting and improve generalization.
Some examples of hyperparameters are the regularization constants in linear models and support vector machines (SVMs), the number of estimators and the max-depth in decision trees, and the number of nodes or the learning rate in deep neural networks.
Hyperparameter tuning or hyperparameter optimization is the process of finding the best hyperparameter values for a given machine learning algorithm and a given dataset.
There are various hyperparameter optimization methods, including grid search, random search, and sequential models, usually involving Bayesian optimization.
Hyperparameter optimization algorithms consist of a search space, a search algorithm, a cross-validation scheme to find the optimal values while avoiding over-fitting, and an objective function with the classifier or regression model and the metric to optimize.
Throughout the tutorials, you will learn each and every aspect of the tuning methods.
In this course, you will learn multiple hyperparameter optimization methods to find the best set of hyperparameters for your classifier or regression models.
You will learn the following search algorithms:
We'll take you step-by-step through engaging video tutorials and teach you everything you need to know about to find the best combination of hyperparameters. Throughout this comprehensive course, we cover almost every available approach to optimize hyperparameters, discussing their rationale, their advantages and shortcomings, the considerations to have when using the technique, and their implementation in Python.
