Course Overview

What are hyperparameters?

Hyperparameters are values that are configured in a machine learning (ML) model before it undergoes training. Their main purpose is to control the behavior of the model during the training process.

The learning rate for gradient descent is one example of a hyperparameter. Other examples of hyperparameters include the number of trees in a random forest and the number of neurons in a neural network layer.

The choice of hyperparameters can have a significant effect on the performance of an ML model. For example, using a higher learning rate can cause the ML model to overshoot the optimal solution while using a lower learning rate can result in a prolonged training process. Similarly, an increase in the number of trees in a random forest algorithm might result in overfitting, while a decrease can result in underfitting.

Therefore, determining the optimal hyperparameters is often a time-consuming and iterative process. It requires training multiple models with different combinations of hyperparameters and the evaluation of their performance to determine the right combination that produces the best results.

Introduction to the course

It’s possible to improve the performance of an ML model using different approaches, such as by increasing the size of the dataset, carrying out different methods of feature engineering, and selecting diverse features. Optimization of hyperparameters, also known as hyperparameter tuning, is the alternative approach to improving the performance of ML models, and it will be the primary focus of this course. Hyperparameter optimization is one of the most important aspects of ML projects, and it has a significant impact on ML models.

Learning about hyperparameter optimization enables data scientists to comprehend how various hyperparameters interact with one another and influence the ML model’s performance. This information is essential for assisting data scientists in making informed decisions regarding the hyperparameters they use in their ML models, as well as determining when the ML model is not functioning properly and what adjustments must be made.

Knowledge of hyperparameter optimization can assist data scientists in avoiding typical mistakes, such as selecting hyperparameters that are too small or too large, which can lead to underfitting or overfitting. Overall, hyperparameter optimization is an essential component of ML that plays a significant part in the creation of accurate and efficient models.

In this course, we’ll explore various hyperparameter optimization techniques that we can apply to improve the performance of ML models, such as the random search method, grid search method, and sequential model-based optimization method.

The intended audience

This course targets beginners in hyperparameter optimization who want to learn various hyperparameter optimization techniques and how to apply them in ML projects. It’s also suitable for data scientists and AI practitioners who are familiar with Python and are interested in improving the performance of their ML models.

Prerequisites

It is important to have a solid understanding of the Python programming language as well as experience with ML in order to successfully complete this course. Moreover, knowledge of the scikit-learn, pandas, and NumPy Python libraries is also required.

Learning outcomes

By the end of this course, we’ll have learned the following:

• Hyperparameter optimization and its benefits.

• Various techniques of hyperparameter optimizations that can be applied to improve the performance of ML models.

• The advantages and disadvantages of each technique for hyperparameter optimization.

We hope that every step of the learning adventure goes well for the entirety of this course. Don’t forget to practice hyperparameter optimization techniques as much as possible in order to become an expert in the subject.